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\title{UTD-HLTRI at SemEval 2026 Task 4: Reasoning like an Expert for Inferring Narrative Similarity }

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\author{
Rakshitha Rao Ailneni, Maitry Bhavsar, and Sanda M. Harabagiu \\
Human Language Technology Research Institute \\
The University of Texas at Dallas \\
\texttt{\{rxa220074, maitry.bhavsar, sanda\}@utdallas.edu}
}
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\begin{document}
\maketitle


\begin{abstract}
Narrative similarity is a challenging problem that requires reasoning over three {\em aspects} of narratives, including (1) the abstract theme; (2) the course of action and (3) the outcomes of narratives.  We present {\bf \textsc{UTD.HLTRI-SIM.Narratives}}, our method developed  for SemEval 2026 Task 4 (Narrative Story Similarity), which combines contrastive reasoning prompting with careful selection of few-shot examples to guide a Large Language Model (LLM) toward decisions of narrative comparative similarity. A curriculum learning framework orders examples of narrative triplets presented to the LLM by using a score that quantifies the impact of common  narratives aspects with information discerned from several {\em distractors} of narrative similarity between pairs of narratives \footnote{Code:\url{https://anonymous.4open.science/r/HLTRISimNarrativesSemEvalTask42026/}}. 
%Specifically, we design a few-shot framework that orders demonstrations from easy to difficult and uses structured contrastive annotations to explicitly compare candidate stories across three narrative aspects: abstract theme, course of action, and outcomes. This design encourages models to focus on underlying causal alignment while ignoring misleading lexical or stylistic distractors. 
%The evaluation results show that our approach substantially outperforms embedding-based baselines and standard zero-shot prompting.
%achieving a test accuracy of 0.742. 
%These results suggest that structured reasoning and curriculum-based prompting enable LLMs to approximate expert-level judgments in narrative similarity tasks.

\end{abstract}

\section{Introduction}
The computational modeling of narrative similarity can be valuable for tasks such as Question Answering, and information retrieval, enabling tools capable to analyze collections of real or fictional narratives, as pointed out in \cite{chaturvedi-etal-2018-heard}. For example, given a news story, a digital archives analyst might identify similar stories from the past. Additionally, similarities between movie scripts can also be detected by this technology, informing creative writing on new scenarios. Several methods for measuring the similarity between texts are available, and they have been also applied to narrative similarity. For example, Story-Emb~\cite{hatzel-biemann-2024-story} generates narrative embeddings from story summaries extracted from Wikipedia, to be used in measuring similarity. In contrast, \cite{chaturvedi-etal-2018-heard} represents narratives 
in terms of plot events, and resemblances between characters and their social relationships to create a {\em story kernel} that measures similarity between pairs of stories. 
In addition \cite{Nguyen2014UsingCT} reported on the various dimensions considered by  experts and non-experts when judging narrative similarity.

%a critical challenge in natural language understanding. Unlike standard semantic similarity, which relies heavily on lexical overlap or shared topics, narrative similarity requires evaluating the abstract, underlying patterns of causality and progression that bind a story together \cite{hatzel2026nsnrl}. Accurately quantifying these relationships holds immense practical utility across various domains, including detecting intellectual property (IP) infringement \cite{chun-2024-aistorysimilarity}, mapping cultural diffusion \cite{waight-etal-2026-quantifying}, and guiding human-AI creative collaboration. 

\begin{figure}[ht]
   \centering
   \includegraphics[width=\linewidth]{images/Example.png}
    \caption{Example of narratives considered for comparative similarity judgment in SemEval 2026 Task 4.}
    \label{fig:Example}
\end{figure}


    

Task 4 of SemEval 2026 \cite{hatzel2026nsnrl, hatzel-etal-2026-semeval} aims to evaluate comparative narrative similarity, not simple similarity between pairs of narratives, as done before in the literature.
In Task 4 of SemEval 2026 comparative narrative similarity is cast as a binary selection problem, in which given a triplet of narratives: $[${\em Anchor Narrative, Narrative A, Narrative B}$]$ systems have to decide which of the pair of narratives $(${\em Narrative A, Narrative B}$)$ is most similar to the {\em Anchor Narrative}. Figure~\ref{fig:Example} illustrates an example of a narrative triplet, in which {\em Narrative A} is judged more similar to the {\em Anchor} narrative than {\em Narrative B}. To justify the comparative similarity decision, the organizers of the task have instructed their annotators to consider three {\bf narrative aspects}, defined as:
\begin{itemize}[nosep]
    \item \underline{{\em A1: Abstract Theme of a narrative:}} The defining constellation of problems, central ideas, and core motifs (excluding concrete setting).
    \item {\underline{\em A2: Course of Action of a narrative:}} The sequence of events, actions, conflicts, turning points, and the order in which they occur.
    \item {\underline{\em A3: Outcomes of the narrative:}} The results of the plot at the end of the text (e.g., conflict resolution, characters' fates, moral lessons).
\end{itemize}
Furthermore, the annotators were not provided any guidance on how to weight the narrative aspects when comparing {\em Narrative A} to the {\em Anchor Narrative}, or respectively {\em Narrative B} to the {\em Anchor Narrative}. A reading of the {\em Anchor Narrative} and {\em Narrative A} illustrated in Figure~\ref{fig:Example} shows that they share the {\em abstract theme}: i.e. "building a successful life through partnership and sustained joint effort", whereas their course of action is different. But most importantly, the {\em Anchor} and {\em Narrative A} have the same outcome: a successful life. In contrast, the {\em abstract theme} of  {\em Narrative B}: "relations between neighbors" is different than the {\em abstract theme} of the {\em Anchor}. In addition, their {\em outcomes} are also different. But, in their respective {\em course of action}, both the {\em Anchor Narrative} and {\em Narrative B} use the same characters: Alice and Bob, that is {\bf distracting} the narrative comparative similarity assessment. 

While the Task 4 of SemEval-2026 had two tracks: {\bf Track A: comparative Narrative Similarity} and {\bf Track B: Narrative Representation Learning}, we chose to participate only in Track A, since our interest was led by curiosity on the capabilities of current Large Language Models (LLMs) to reason with the content of narratives, rather than on their capabilities to participate in the distillation of narrative representations. Our participation in Track A was inspired by the annotation guidelines, aiming to mimic the judgments of experts for comparative similarity of narrative, which explains the title of our paper. 

\begin{figure*}[t]
   \centering
   \includegraphics[width=\linewidth]{images/Architecture.png}
    \caption{The Architecture of the \textsc{UTD.HLTRI-SIM.Narratives} system.}
    \label{fig:Architecture}
\end{figure*} 

Our system, {\bf \textsc{UTD.HLTRI-SIM.Narratives}}, is considering, in addition to the {\em narrative aspects}, a set of {\em narrative similarity distractors},  which contribute the the quantification of the {\em difficulty} of assessing the comparative similarity for a narrative triplet.  
%This enables us to define a difficulty metric  that informs  {\em curriculum learning}
%of narrative triplets. 
Narrative triplets are selected for generating demonstrations used in the Chain-of-Thought (CoT)  \cite{CoT} prompting of an LLM. The narrative triplets are presented to the LLM using an order than goes from the least difficult narrative triplet to more difficult ones. 
In this  few-shot learning scenario the LLM prompting in  the \textsc{UTD.HLTRI-SIM.Narratives}system, it  requires {\em contrastive reasoning}.\\  

\begin{comment}
\begin{table*}[t]
\small
\centering
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.5}

\begin{minipage}{\textwidth} % <-- reduce this (e.g., 0.85)
\centering
\begin{tabularx}{\textwidth}{@{} 
  >{\raggedright\arraybackslash\hsize=0.3\hsize}X 
  >{\raggedright\arraybackslash\hsize=0.8\hsize}X 
  >{\raggedright\arraybackslash\hsize=2\hsize}X 
  >{\raggedright\arraybackslash\hsize=1.0\hsize}X 
  @{}}
\toprule
\textbf{Story} & \textbf{Theme} & \textbf{Course of action} & \textbf{Outcome} \\
\midrule

\textbf{Anchor} & 
Tradition vs modernity; family/parenthood complexities. & 
Sasthri cuts ties with modern doctor Bharathi $\rightarrow$ Parvathi gets pregnant and seeks Bharathi’s medical help $\rightarrow$ she gives birth and also adopts another newborn $\rightarrow$ Sasthri is confused about which child is biological $\rightarrow$ adopted child is raised in the family while biological son grows up elsewhere $\rightarrow$ the two boys later become friends $\rightarrow$ reconciliation follows & 
Family reunites; both children are accepted; divide between tradition and modernity is bridged. \\

\textbf{Text A} & 
Justice + mother–child bond under legal/social barriers. & 
Woman is falsely accused for husband’s crime $\rightarrow$ imprisoned $\rightarrow$ gives birth in prison $\rightarrow$ baby is put up for adoption $\rightarrow$ new evidence frees her $\rightarrow$ she searches for her daughter & 
She is cleared and begins the reunion quest with her child. \\

\textbf{Text B} & 
Identity search; emotional complexity of adoption/family ties. & 
Ben is raised by adoptive parents $\rightarrow$ decides to meet biological parents $\rightarrow$ learns biological mother has died $\rightarrow$ finds biological father is in psychiatric care $\rightarrow$ processes disappointment/loss & 
Confronts painful reality of biological family; reflects on identity and what ``family'' means. \\

\bottomrule
\end{tabularx}
\end{minipage}

\caption{Story Themes, Sequences, and Outcomes}
\label{tab:story_analysis}
\end{table*}
\end{comment}


%Table \ref{tab:story_analysis} illustrates a motivating example of this difficulty. Actual stories are shown in appendix \ref{app:motivating_example}. The anchor story intertwines a cultural conflict with adoption-driven identity ambiguity. Candidate A shares the anchor’s physical event sequence (the Course of Action), whereas Candidate B aligns powerfully with the abstract Theme of identity search but lacks the matching multi-actor events. Resolving such conflicts requires reasoning over character roles, temporal structures, and overarching motifs without being distracted by localized keywords \cite{chambers-jurafsky-2009-unsupervised, mostafazadeh-etal-2016-corpus}

%, a binary classification task where computational methods must identify which of two candidate stories is narratively more similar to an anchor story. Figure \ref{fig:Task_introduction} gives the task description where \emph{Judgement Module} compares both the candidate stories with anchor story to identify which story is more similar to anchor story.  This task is particularly difficult because it requires models to look past adversarial, surface-level lexical overlaps to evaluate strict, underlying structural alignments. To formalize this objective evaluation, the task organizers established a theoretical framework decomposing similarity into three measurable aspects: \textit{Abstract Theme}, \textit{Course of Action}, and \textit{Outcomes} \cite{hatzel2026nsnrl, guidelines2026}. The core challenge lies in rigorously evaluating these three dimensions across highly deceptive texts, rather than relying on flawed keyword matching.

%%%%%%%%%%%%%%%%%%%%%%%%%

%Traditional methods for narrative similarity have historically proven insufficient for such nuanced conflicts. Early symbolic approaches modeled stories using rigid narrative units \cite{lehnert1981plot}, which captured structural overlaps but lacked thematic nuance. Conversely, modern dense vector representations like Sentence-BERT \cite{reimers2019sbert} and Story-Emb \cite{hatzel-biemann-2024-story} struggle to distinguish between shared surface settings (e.g., matching character names or locations) and deep structural similarity. 

%Our solution to inferring the most similar narrative to an {\em Anchor Narrative} is Large Language Models (LLMs) through a few-shot \textbf{aspect-based contrastive reasoning} and \textbf{in-context curriculum learning} \cite{bengio2009curriculum, wang2023curriculum, su2022contrastive}. 

%???Instead of asking the model for an absolute similarity score or providing a random set of examples, our method uses a curriculum mechanism with aspect-based contrastive reasoning annotations. This mechanism orders in-context examples from easy to hard, while the contrastive annotations force the model to explicitly evaluate the differences between the candidates relative to the anchor across the three predefined axes. This makes the method highly efficient at isolating the correct narrative structure from text.\\
%Our primary contribution is HLTRI-SIMNarratives, an LLM-based method that transforms static annotation guidelines into an efficient few-shot framework using aspect-based contrastive reasoning to guide narrative similarity judgments while reducing reliance on surface-level lexical cues. We make available the code as all prompts that we used on GitHub\footnote{\url{}}.

%The remainder of this paper is organized as follows. Section \ref{sec:rel} reviews related work, Section \ref{sec:dataset} describes the NSNRL dataset, Section \ref{sec:method} presents our methodology, Section \ref{sec:exp} reports experimental results, and Section \ref{sec:concl} concludes with key findings and future directions.

%Our primary contributions are threefold: (1) we introduce \textbf{HLTRI-SIMNarratives}, an LLM-based method that transforms static annotation guidelines into an efficient few-shot algorithm; (2) we provide empirical evidence that using aspect-based contrastive reasoning annotations successfully mitigates surface-level lexical distractions; and (3) we demonstrate that a holistic, context-preserving LLM evaluation outperforms multi-stage extraction pipelines, showing that aggressive segmentation can destroy subtle causal links.










\section{The NSNRL Dataset}
\label{sec:dataset}
The Narrative Story Similarity and Narrative Representation Learning (NSNRL) dataset is the official benchmark for SemEval 2026 Task 4 \cite{hatzel2026nsnrl}. Sourced from English Wikipedia film summaries from the Tell-Me-Again corpus \cite{hatzel2024tellmeagain}, this dataset comprises 1,039 narrative triplets, from which a Development Set of 200 narrative triplets was created, as well as a Test Set of 400 narrative triplets. Texts from the narrative triplets were strictly filtered to contain 4-8 sentences, thus providing a high density of narrative progression.
However, we have noticed that the number of words in the narratives varies substantially, as illustrated in 
Table~\ref{tab:words_narratives}. This indicates than many narratives are longer, in terms of the number of words they use, than those illustrated in Figure~\ref{fig:Example}. 

\begin{table}[h]
\small
\centering
\begin{tabular}{|lll|}
\hline
   & {\bf Development Set}  & {\bf Test Set} \\ 
{\em Shortest Narrative} & 36 words & 35 words \\
{\em Longest Narrative} &  304 words&  355 words \\
{\em Average \#  words } & &  \\ 
{\em per narrative} &  123.3 words  &  121.4 words \\ \hline  
\end{tabular}
\caption{Number of words in narratives.}
\label{tab:words_narratives}
\end{table}


An important attribute of the NSNRL dataset is that it is highly adversarial. Candidate narratives were generated using LLM prompting to create \emph{hard negatives} namely, narratives exhibiting high surface-level semantic similarity to the anchor narrative, but drastically diverging in their narrative aspects. %A prospective triple was only advanced to human annotators if distinct commercial LLMs disagreed on the outcome \cite{hatzel2026nsnrl}. Ground truth labels require explicit agreement between at least two human experts, highlighting the extreme cognitive load required to differentiate candidate stories.

\begin{comment}
\section{The Method}
\label{sec:method}
\begin{figure*}[t]
  \centering
    % Replace 'filename' with your image file name (e.g., image.png, image.jpg)
    \includegraphics[width=0.9\textwidth]{latex/methodologynew1.pdf}
    \caption{\textbf{HLTRI-SIMNarratives overview:} (A) prompt pipeline; (B) demonstrations are contrastively annotated and then curriculum-ordered using the difficulty score.}
  \label{fig:method_overview}
\end{figure*}
\end{comment}

\section{The Method}

The architecture of the  \textsc{UTD.HLTRI-SIM.Narratives} system is illustrated in Figure~\ref{fig:Architecture}. 
We start by selecting a few examples which were provided by the organizers of the Task 4 of SemEval-2026 as instructions for their annotators. Since these examples are meant to be used in a few-shot learning scenario for prompting an LLM, we also considered using {\em curriculum learning}, such that examples are presented in order of their difficulty, enabling the LLM to first learn from simpler examples before being presented with more difficult examples. This observation proved helpful in our past experiments~\cite{weinzierl-harabagiu-2024-discovering} with CoT prompting of LLMs.

The structured prompting that we have used, detailed in Appendix~\ref{app:main_method}, defines the narratives aspects, and instructs the LLM to use {\em contrastive reasoning} when deciding the {\em comparative similarity} between (1) the {\em Narrative A} and the {\em Anchor Narrative}; and (2) the {\em Narrative B} and the {\em Anchor Narrative} of each narrative triplet.  The structured prompt also requires the LLM to generate a rationale for its decision. 

%The simple architecture of the { UTD.HLTRI-SIM.Narratives} system is impacted by (1) the selection decision for the examples of narrative triplets; (2) the order of presentation of these examples, provided by curriculum learning; and (3) the contrastive reasoning used in the structured prompt. 
%We detail these factors further bellow:\\
\subsection{ Selection of Examples of Narrative Triplets}
The annotators for this task were instructed to consider the three possible {\em narrative aspects} when performing their comparative similarity judgments for the narrative triplets. They were also told that while distinguishing the three aspects of narratives can be challenging, it is important to consider each aspect independently. They were provided with five guideline examples and ten additional training examples. After carefully examining these examples, we have selected nine of them. Our selection followed principles of {\em complexity} and {\em diversity}. For example, when considering complexity,  we selected examples in which (a) all three narrative aspects were matched between the most similar narrative and the anchor narrative; (b) only two  narrative aspects were matched between the most similar narrative and the anchor narrative; and (c) only one narrative aspect was matched between the most similar narrative and the anchor narrative. In terms of diversity, we selected examples in which different narrative aspects were matched between the most similar narrative and the anchor narrative. The examples that we have selected are listed in Appendix~\ref{app:examples}.




\begin{comment}
    
is a few-shot prompting framework built on top of a large language model. The prompt consists of three components: (i) system instructions, (ii) curriculum-ordered demonstrations, and (iii) the user query. Each demonstration contains contrastive explanations that compare Story A and Story B with respect to an anchor story across predefined narrative aspects. To support curriculum learning, we introduce a difficulty scoring mechanism that orders demonstrations from easier to harder examples. The scoring function considers both the number of matching aspects between the anchor and the selected story and the degree of similarity between the anchor and the non-selected story. In addition, it accounts for the presence of \emph{distractors} shared between the anchor and each candidate story. Figure~\ref{fig:method_overview} provides an overview of the complete framework.\\
The system prompt encodes expert-style annotation guidelines for narrative similarity. It defines the task objective, specifies the three narrative aspects (Abstract Theme, Course of Action, and Outcomes), and instructs the model to ignore distractor cues such as writing style, character names, setting details, and other surface-level signals. The full prompt text is provided in Appendix~\ref{app:main_method}.
%, and enforces aspect-wise contrastive comparison with tie-breaking guidance. 
\end{comment}
\begin{comment}
    

\subsection{Narrative Aspects}
Traditional semantic similarity methods often rely on surface-level overlap, such as shared keywords or named entities. In contrast, \emph{aspect-based structured narrative similarity} evaluates stories based on their underlying conceptual structure rather than their vocabulary. Following the SemEval-2026 Task 4 guidelines, our approach grounds comparison in three core aspects:
\begin{enumerate}[nosep, label=\arabic*.]
    \item \textbf{Abstract Theme:} The defining constellation of problems, central ideas, and core motifs (excluding concrete setting).
    \item \textbf{Course of Action:} The sequence of events, actions, conflicts, turning points, and the order in which they occur.
    \item \textbf{Outcomes:} The results of the plot at the end of the text (e.g., conflict resolution, characters' fates, moral lessons).
\end{enumerate}
Detailed examples of these aspects are provided in Appendix~\ref{app:aspects}.
\subsection{Contrastive Demonstrations}
The HLTRI-SIMNarratives prompt includes few-shot demonstrations designed to guide the model in making difficult narrative comparisons through contrastive reasoning. Inspired by contrastive learning, which separates similar and dissimilar examples to sharpen decision boundaries \cite{hadsell2006dimensionality,su2022contrastive}, our demonstrations explicitly model comparative reasoning within the prompt.\\
For each demonstration consisting of an (Anchor, Story A, Story B) triplet, contrastive reasoning is performed aspect by aspect. For every narrative aspect, the explanation compares the anchor story with the selected story, describing why that aspect aligns, and simultaneously contrasts the anchor with the non-selected story, explaining why the same aspect does not match. This dual explanation helps the model distinguish true structural similarity from misleading surface overlap. Each demonstration is annotated using a structured four-step format:
\begin{enumerate}[nosep, label=(\arabic*)]
\item \textbf{Contrastive Reasoning:} Aspect-wise comparison explaining why the selected story aligns with the anchor and why the non-selected story diverges.
\item \textbf{Selected Story:} Explicit identification of the chosen candidate.
\item \textbf{Contributing Aspects:} The narrative aspects that meaningfully support the selection.
\item \textbf{Final Explanation:} A concise summary of the structural similarities and differences that justify the final decision.
\end{enumerate}
\end{comment}


\subsection{Curriculum Learning}

The examples of narrative triplets that were selected were aimed to be presented in a few-shot learning scenario to the LLM. However, we also realized that the LLM will learn better if 
the examples would be presented in a {\em curriculum} \cite{bengio2009curriculum} which is:
(1) ranking of examples
in terms of difficulty; and (2) transitioning from easy
to difficult examples during training. 
%The sequence in which in-context demonstrations are presented can materially affect model performance. We therefore adopt a curriculum-style ordering, where demonstrations are arranged from easier to harder comparisons \cite{bengio2009curriculum, wang2023curriculum}. 
For comparative narrative similarity, example difficulty varies across narrative triplets due to (i) the number of narrative aspects matched against the narrative anchor and (ii) the number of {\em narrative similarity  distractors} that are present in narratives that are compared. We have defined five different narrative similarity distractors:
\begin{itemize}[nosep]
    \item \underline{\em D1: Narrative Beginning:} The anchor narrative and the least similar narrative share the same beginning. 
    \item \underline{\em D2: Common Setting:} The anchor narrative and the least similar narrative share the same spatial and temporal grounding.
    \item \underline{{\em D3: Common entities, similar events:}} The anchor narrative and the least similar narrative share narrative characters or similar events.
    \item \underline{\em D4 Common Emotions:} The anchor narrative and the least similar narrative share the same emotional framing.
    \item \underline{\em D5: Common aspects of background:} The anchor narrative and the least similar narrative share commons aspects of their respective narrative backgrounds.
\end{itemize}

The definition of these narrative similarity distractors informs the quantification of their impact on the comparative similarity decision through a function $D(N_X, N_A)$  which computes the number of narrative similarity distractors observed between a narrative $N_A$ (which could be either the {\em Narrative A} or  the
{\em Narrative B} or a narrative triplet, and $N_A$, the {\em Anchor Narrative} or the triplet. Evidently, $D(N_X,N_A) \in\{0,\dots,5\}$. For each of the nine selected examples, we have manually quantified the values of 
$D(N_X,N_A)$ for the {\em Narrative A} and  the {\em Narrative B} of the example.

Since the narrative aspects of each example had to also be considered, we have defined a function $A(N_X, N_A)$
which computes the number of common narrative aspects observed between a narrative $N_X$ (which could be either the {\em Narrative A} or  the
{\em Narrative B} of an example), and $N_A$, the {\em Anchor Narrative} or the example. Evidently, $A(N_X,N_A) \in\{0,\dots,3.\}$ For each of the nine selected examples, we have manually quantified the values of 
$A(N_X,N_A)$ for the {\em Narrative A} and  the {\em Narrative B} of the example.


%We define a single difficulty score for each triplet and sort demonstrations by ascending score.

\begin{comment}
\begin{figure*}[t]
  \centering
    % Replace 'filename' with your image file name (e.g., image.png, image.jpg)
    \includegraphics[width=\textwidth]{images/aspects.jpg}
    \caption{Example of narrative aspects and distractors. For Course of Action, E = Event, A = Action, TP = Turning Point, C = Conflict.}
  \label{fig:method_overview}
\end{figure*}
\end{comment}

\begin{comment}

\subsubsection{Narrative Distractors}
We define a \textbf{distractor} as a surface-level overlap between stories that creates a false impression of similarity without reflecting true narrative alignment. Distractors arise because LLMs tend to rely on token co-occurrence and shallow semantic cues, such as shared settings, entities, or emotional tone, which can overshadow deeper structural similarities in theme or course of action. We emphasize that distractors manifest differently depending on whether a story is \emph{selected} or \emph{non-selected}: for the \emph{non-selected} story, distractors are typically \emph{similarities} to the anchor that can make it appear falsely closer; for the \emph{selected} story, distractors are typically \emph{differences} from the anchor that can make it appear falsely less similar. To address this, we identify and penalize five categories of distractors:



\begin{table*}[t]
\centering
\small
\setlength{\tabcolsep}{6pt}
\renewcommand{\arraystretch}{1.2}
\caption{Qualitative example with distractors and difficulty. C1=Theme, C2=Course of Action, C3=Outcome.}
\label{tab:lost_item_example}
\begin{tabular}{ p{0.8\textwidth} p{0.2\textwidth}}
\toprule
\textbf{Stories} & \textbf{Labels} \\
\midrule

\textbf{Anchor:} Anna loses her purse. She retraces her steps but cannot find it. Dan finds it and helpfully returns it to her. \par
\textbf{Story A:} Brian lost his backpack. He was terrified because there were important documents in it. After an hour of intense search he finally found it. \par
\textbf{Story B:} Alex lost his engagement ring while swimming. After hours of looking, he still can not find it. Karen finds the ring while magnet fishing and, based on the engraved name, manages to return it.
&
\textbf{Selected:} B \par
\textbf{Contrib(A):} C1 \par
\textbf{Contrib(B):} C1, C2, C3 \par
\textbf{Distr(Anch--A):} D1 \par
\textbf{Distr(Anch--B):} D2 \par
\\
\\
\textbf{Anchor:} After an accident, Neo, a lonely man in his 40s, loses his eyesight. He learns to handle the new challenges the world now has for him. He makes a new friend, Anna, who is very willing to help him out, finding connections that he never thought possible before. \par
\textbf{Story A:} Adam has a large circle of friends. One day, he loses his hearing in an accident. He struggles with the new challenges in communication, alienating his friends, and finally becomes very lonely. \par
\textbf{Story B:} Brian is a London-based artist who creates street art. He leads a lonely life. One day, he encounters a fellow artist who is painting what looks to be an identical picture to the one he is working on. The two make a deep connection over their shared love for art and remain friends to this day.
&
\textbf{Selected:} B \par
\textbf{Contrib(A):} C1, C2 \par
\textbf{Contrib(B):} C1, C2, C3 \par
\textbf{Distr(Anch--A):} D1, D3 \par
\textbf{Distr(Anch--B):} D1, D2 \par
\\

\bottomrule
\end{tabular}
\end{table*}
\end{comment}
For each narrative triplet example $\mathcal{T}=(N_A,N_{XA},N_{XB})$, where $N_A$ represents the {\em Anchor Narrative}, $N_{XA}$ represents {\em Narrative A}, and $N_{XB}$ represents {\em Narrative B}, we also had access to $X$, which indicates which narrative was \underline{ most similar} to $N_A$, where $X$ could be either ${XA}$ or ${XB}$. 
We also are aware of $Y$, which indicates which narrative was \underline{least similar} to $N_A$, 
where $Y$ could be either ${XA}$ or ${XB}$. This enables us to define the difficulty score of each example narrative triplet $\mathcal{T}=(N_A,N_{XA},N_{XB})$ as:
\begin{equation}
\label{eq:difficulty_score}
\begin{align}
D\_Score(\mathcal{T}) \;=\;& \alpha\bigl(3 - A(N_{X}, N_{A})\bigr) \nonumber\\
&+ \beta\,A(N_{Y}, N_{A})\nonumber\\
&+ \gamma\,D(N_{Y}, N_{A}) \nonumber\\
&+ \delta\,D(N_{X}, N_{A}) 
\end{align}
\end{equation}
%where $A(S, S_{\text{anchor}})\in\{0,1,2,3\}$ is the number of matching narrative aspects between story $S$ and the anchor,
%and $D(S, S_{\text{anchor}})\in\{0,\dots,5\}$ is the number of distractor types (from $\{D_1,\dots,D_5\}$) shared between $S$ and the anchor.

$\Box${\underline{{\em Intuitions:}} When defining the $D_{Score}$, we used several intuitions of the difficulty of performing comparative similarity decisions. First, we believe that when fewer common narrative aspects exist between the $N_A$, the {\em Anchor Narrative} and $N_X$, the narrative deemed to be most similar to $N_A$, it is more difficult to make the comparative similarity decision, therefore we used the term $(3-A(N_{X}, N_{A})$, which increases by the number of common narrative aspects between $N_A$ and $N_Y$ that are observed. Second, if the least similar narrative $N_Y$ also shares some narrative aspects with $N_A$, it should also contribute to the value of $D_{Score}$, hence the usage of the term $A(N_Y,N_A)$. Third, we also account for the number of narrative distractors observed between $N_Y$ and $N_A$, and $N_X$ and $N_A$ respectively. The coefficients $\alpha, \beta, \gamma,$ and $\delta$ were selected empirically using the development set. We evaluated several combinations 
%and ranked the demonstration examples according to the resulting $D\_Score$. Each configuration was then used to order the few-shot demonstrations, and the downstream narrative similarity accuracy was measured. 
and the values $\alpha = 2$, $\beta = 2$, $\gamma = 1.5$, and $\delta = 1.5$ yielded the best validation accuracy and were therefore used in our final setup.\\ 
%The values for $\alpha$, $\beta$, $\gamma$ and $\delta$ were set as 2, 2, 1.5, 1.5. \\
%anchor on one or more aspects, tightening the decision boundary. The distractor terms increase the score when superficial overlaps are present.
The difficulty score $D\_{Score}$ produced an ordering of the selected examples, which were presented as few shot examples to the LLM.
%We sort in-context demonstrations by ascending $Score(\mathcal{T})$, yielding a curriculum from the most unambiguous to the most ambiguous comparisons.

\subsection{ Structured Prompting of the LLM}

The prompting of the LLM is first used in the few shot learning scenario, where demonstrations accounting for the selection of the most similar narrative to an {\em Anchor Narrative} are presented, guiding the contrastive reasoning of the LLM. Examples of such demonstrations are provided in Appendix~\ref{app:demo_examples}.  After the LLM is presented with the nine examples and their demonstrations, the LLM is then prompted with examples from the Test Set, being required to {\em produce rationales} for its decisions. The rationales inform the error analysis of the decisions make by the LLM.

\begin{comment}
    

The sequence in which in-context demonstrations are presented fundamentally alters model performance. Curriculum learning involves introducing examples in an order of gradually escalating difficulty. Recent benchmark studies on large language model context windows demonstrate that an escalating curriculum format stabilizes attention heads and allows the model to map complex instructions efficiently without being overwhelmed by immediate complexity \cite{bengio2009curriculum, wang2023curriculum}. 

For narrative similarity, computational difficulty is highly variable. We leverage a two-stage algorithmic process to present the most unambiguous, clear-cut comparisons first, gradually progressing to the most distractor-heavy, ambiguous triplets.

\paragraph{Stage 1: Difficulty Tiering.} 
The baseline tier is determined strictly by the number of aspects the ground-truth selected candidate ($S_{pref}$) shares with the Anchor story ($S_{anchor}$). We define three categorical tiers: \textbf{Easy} (matches all 3 aspects), \textbf{Medium} (matches exactly 2 aspects), and \textbf{Hard} (matches only 1 aspect).

\paragraph{Stage 2: Intra-Tier Difficulty Scoring.}
Within each tier, the true computational complexity of a triplet ($\mathcal{T}$) is driven by the presence of matching aspects in the non-selected story ($S_{non}$) and the total volume of deceptive distractors. For sorting examples \textit{within} a specific tier, we apply the following scoring mechanism:

\begin{equation}
Score(\mathcal{T}) = A(S_{non}, S_{anchor}) + \sum_{k=1}^{5} D_k(\mathcal{T})
\end{equation}

Where $A(S_{non}, S_{anchor})$ represents the number of matching narrative aspects ($0 \leq A \leq 3$) between the non-selected story and the anchor, and $D_k(\mathcal{T}) \in \{0, 1\}$ denotes the presence of a specific distractor within the triplet. 

As the non-selected story shares more aspects (creating a tighter decision boundary) and as the volume of distracting cues ($D_1 \dots D_5$) increases, the calculation yields a higher complexity score. Demonstrations are placed in the prompt sorted first by Tier (Easy to Hard), and subsequently ordered within their respective tiers by ascending $Score(\mathcal{T})$.




Table~\ref{tab:lost_item_example} presents two qualitative examples showing distractor extraction.


\paragraph{\textbf{Example 1(Lost item):}} The non-selected $S_A$ triggers $D1$ because it starts with the same premise as the anchor (a lost item and a search: ``lost his backpack'', ``intense search''), which can make it look similar even though it misses the anchor's key step where a third person returns the item (anchor: ``Dan finds it and \dots returns it'' vs.\ $S_A$: ``he finally found it''). The selected $S_B$ triggers $D2$ because its concrete setting details (``swimming'', ``magnet fishing'') look different from the anchor's everyday context, even though its core structure still matches (failed search $\rightarrow$ stranger finds $\rightarrow$ returns).

\paragraph{\textbf{Example 2(Disability vs.\ friendship):}} The non-selected $S_A$ triggers $D1$ and $D3$ because it shares the same starting premise and surface cues with the anchor (``loses his hearing in an accident'' vs.\ ``loses his eyesight''); this can make $S_A$ look correct even though it ends in the opposite direction (anchor: ``makes a new friend'' vs.\ $S_A$: ``finally becomes very lonely''). The selected $S_B$ triggers $D1$ and $D2$ because it begins differently and in a different setting (no accident/disability; ``London-based artist''), which can make it look less related at first glance, even though it matches the anchor's main progression and ending (lonely person meets someone $\rightarrow$ deep connection $\rightarrow$ lasting friendship).
\end{comment}
%\subsection{User Prompt}
%The final component of the prompt is the active User Query. After processing the system guidelines and internalizing the complex decision boundaries established by the curriculum-ordered contrastive demonstrations, the model receives the unannotated triplet (Anchor, Text A, Text B) to evaluate and generate its final structured prediction.



\section{Experimental Results}
\label{sec:exp}

To prepare for our submission in the Task 4 of Semeval-2026, we have considered three baselines, which served in the comparative evaluations of the \textsc{UTD.HLTRI-SIM.Narratives} system on the Development Data and Test Data set. The metric of evaluation was {\em Accuracy}, which measures the number of times the most similar narrative to the {\em Anchor Narrative} was predicted out of all the test narrative triplets.

\subsection{The Baselines}


\paragraph{Narrative embeddings:}
Our baseline encodes all the narratives from a triplet as embeddings, using \texttt{all-MiniLM-L6-v2}~\cite{sentence-transformers-all-minilm-l6-v2, reimers-gurevych-2019-sentence}. The prediction of the most similar narrative is informed by the largest cosine similarity between the embedding of the {\em Anchor Narrative} and the embeddings of  \em Narrative A} and {\em Narrative B} respectively. 
%The narrative having the highest cosine similarity is selected.

\paragraph{Zero-shot prompting of LLM}
For a simple zero-shot baseline, we follow the organisers’ reference implementation\footnote{\url{https://github.com/narrative-similarity-task/semeval-2026-task-4-baselines}}.
Given a narrative triplet, we prompt the LLM to decide the comparative similarity between {\em Narrative A} and the {\em Anchor Narrative} or {\em Narrative B} and the {\em Anchor Narrative}. We do not provide any examples to the LLM. 
%is \emph{more narratively similar} to the anchor (i.e., output \texttt{A} or \texttt{B}), without any in-context examples or task-specific tuning.

\paragraph{Plot-centered prompting.}
This baseline considers the importance of discerning the plots of each narrative in order to perform comparative similarity over a narrative triplet. It consists of a sequence of three prompts, detailed in Appendix~\ref{app:plots}

\begin{comment}
\paragraph{{Stage 1 (Normalize):}} Rewrite the anchor and both candidates into a shared, neutral third-person style, reducing surface cues (e.g., proper names, specific time/place details, sentiment-heavy phrasing, and irrelevant lore) while preserving plot-critical content.
\paragraph{{Stage 2 (Segment):}} Convert each normalized story into 4--8 chronological plot beats labeled by their role in the story arc (e.g., \textsc{Premise}, \textsc{Conflict}, \textsc{Turning\_Point}, \textsc{Outcome}). Each beat includes the beat text plus metadata (\texttt{function}, \texttt{gist}, \texttt{story\_effect}).
\paragraph{{Stage 3 (Judge):}} Compare the anchor against Story~A and Story~B across abstract theme, course of action, and outcomes, then output the selected story, the contributing aspects, and a concise contrastive justification.
\end{comment}



\subsection{The results of the \textsc{UTD.HLTRI-SIM.Narratives} system}


{\bf Prompting different LLMs:}  
We evaluate the \textsc{UTD.HLTRI-SIM.Narratives} system  when prompting two LLMs: \texttt{GPT-4o} \citep{openai-gpt4o-2024,openai-api-gpt4o} and \texttt{gemini-2.5-flash-lite} \citep{google-gemini-25-flash-lite,google-vertex-gemini-25-flash-lite} on both the development and test sets.

Table~\ref{tab:baseline_comparison} lists the results on the Development and the Test Data when prompting each LLM. The Table also shows the results of the baselines. Prompting \texttt{GPT-4o} generated the best overall results, both on the Development  and on the Test Data. Interestingly the results when prompting both of LLMs decreased by 3\% Accuracy from the Development Data to the Test Data, indicating that the Test Data was probably more difficult than the Development Data. 
%The comparison with the Zero-shot learning scenario, where accuracy is almost maintained from the Development Data to the Testing Data is not surprising. 
However, it was underwhelming to notice that at most 6\% Accuracy gain was obtained when (a) performing few-shot learning; and (b) using contrastive reasoning. This indicates that LLMs have developed capabilities are are strong enough for comparative similarity reasoning, such that a few examples and their demonstrations have limited impact on their selection capability. However, there is still a lot of room to improve on LLM reasoning, as in more than 25\% is the cases, their reasoning was not correct, which allowed us to perform an error analysis of the test results. 

We were also interested to understand the impact on the \textsc{UTD.HLTRI-SIM.Narratives} system of (a) the examples and their demonstrations of narrative comparative similarity; (b) the contrastive reasoning; and (c) the curriculum learning of the examples. For this reason we have conducted ablation studies.

%the s the compares \textsc{HLTRI-SIMNarratives} with three baselines on the development and test sets, and also reports results for \texttt{GPT-4o} and \texttt{gemini-2.5-flash-lite}. For curriculum learning, we use 10 in-context demonstrations from the organizers’ annotation-guidelines document, comprising manually written examples, LLM-generated examples, and examples sampled from the dataset. Detailed annotations of distractors and matching aspects are provided in Appendix~\ref{app:qual_examples}. For the demonstration difficulty score, we set $\alpha=\beta=2$ and $\gamma=\delta=1.5$. Overall, \textsc{HLTRI-SIMNarratives} performs better than the baselines on both splits. 

% Requires: \usepackage{booktabs}
\begin{table}[t]
\centering
\footnotesize
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.1}
\caption{Accuracy results on Development Data and Test Data.}
\label{tab:baseline_comparison}
\begin{tabular}{lcc}
\toprule
\textbf{Method} & \textbf{Dev} & \textbf{Test} \\
\midrule
MiniLM Embedding Similarity & 0.55 & 0.59 \\
Zero-shot Prompting & 0.69 & 0.68 \\
Plot-Centered Baseline & 0.68 & 0.67 \\
\textsc{UTD.HLTRI-SIM.Narratives} (Gemini) & 0.71 & 0.68 \\
\textsc{UTD.HLTRI-SIM.Narratives} (GPT) & 0.77 & 0.74 \\
\bottomrule
\end{tabular}
\end{table}

{\bf Ablation studies.}
We conducted three ablation studies on the Development Data set to quantify the contribution of key components of the \textsc{UTD.HLTRI-SIM.Narratives}  system. %\textsc{HLTRI-SIMNarratives}.
First, we removed the in-context demonstrations of the selected examples.
Second, we remove the contrastive reasoning instructions from the LLM prompts.
Third, we presented the selected examples, but instead of the order produced by the $D_{Score}$ used in the curriculum, we presented the LLM with the same examples and their demonstrations in a  random order.
%instead of using the curriculum ordering.
Table~\ref{tab:ablation_dev} reports the accuracy results.
Overall, the ablation results indicate that the largest gains in accuracy are granted by the usage of contrastive reasoning, followed by the usage of demonstration of selected examples, ranked by their difficulty. 
%performance drops in each ablation, indicating that both the in-context curriculum ordering and the aspect-based contrastive reasoning component contribute to the final accuracy.

\begin{table}[t]
\centering
\small
\setlength{\tabcolsep}{6pt}
\renewcommand{\arraystretch}{1.15}
\caption{Ablation accuracy results on the Development Data.}
\label{tab:ablation_dev}
\begin{tabular}{lc}
\toprule
\textbf{Setting} & \textbf{Acc.} \\
\midrule
\textsc{UTD.HLTRI-SIM.Narratives}  &  \\
%\midrule
w/o demonstrations & 0.71 \\
w/o contrastive reasoning & 0.74 \\
random order of demonstrations & 0.75 \\
\textsc{UTD.HLTRI-SIM.Narratives} & 0.77 \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Error Analysis}

Our inspection of the erroneous decisions of the \textsc{UTD.HLTRI-SIM.Narratives} system and of the rationales that it generated indicates several major types of errors:\\
\underline{\em Error Type 1:} The \textsc{UTD.HLTRI-SIM.Narratives}  system infers that two narratives have the same narrative aspects when in fact that do not do so. Appendix~\ref{Errors} illustrates the analysis of this type of errors.\\
\underline{\em Error Type 2:} The \textsc{UTD.HLTRI-SIM.Narratives} system does not recognize common narrative aspects correctly. missing the inference of common narrative aspects shared by the {\em Anchor Narrative} and the most similar narrative.\\
\underline{\em Error Type 3:} The \textsc{UTD.HLTRI-SIM.Narratives}  system is confused by narrative distractors that make difficult the inference of common narrative aspects, especially when only one common narrative aspect is shared with the {\em Anchor Narrative}.

\section{Discussion}

The results of the ablation studies that we have conducted indicate that the contrastive reasoning of the LLM had the greatest impact on performance of the \textsc{UTD.HLTRI-SIM.Narratives}  system, but it is obvious that the LLM needed further assistance by being guided to a more nuanced understanding of the narrative distractors. Unfortunately, the prompts that we have used did not include any information about those distractors. In addition, we believe that we should also also defined a set of comparative narrative similarity {\em enforcers} which should have been used in the prompting of the LLM system as well. 

In addition, although we have conceptualized several narrative similarity distractors, we have not implemented automatic methods of discerning them, preferring to use ad-hoc human evaluations of these distractors only for curriculum generation. Future work should consider automatic means of evaluating both distractors and enforcers of comparative narrative similarity, which should be included in the LLM prompting.

Finally, we believe that further modeling of the three aspects of the narratives should improve the performance on this task.

\section{Conclusion}
\label{sec:concl}
Our participation in the Task 4 of SemEval-2026 through the \textsc{UTD.HLTRI-SIM.Narratives} system enabled us to conclude that a simple system that uses few-shot learning in which examples are carefully selected and ordered by their level of difficulty for comparative similarity decisions has less impact on results than  contrastive reasoning.  Moreover, prompting an LLM to perform judgments similar those of the annotators helps in improve the results.
As expected, LLM reasoning outperforms embedding-based methods for narrative comparative similarity detection.
Relying on elements of narrative analysis, such as plot structure, does not help with the task of narrative comparative similarity detection. This may be explained by the fact that in the Task 4 of SemEval-2026 the narratives were in fact very short, produced as summaries, where plot structure was minimal.
The complexities of the task of comparative similarity  for narrative triplets require further research, as currently, LLMs generate many errors.  
Our results show that LLMs can closely approximate expert judgments on narrative similarity when guided by structured contrastive reasoning and curriculum-ordered in-context demonstrations. By directing the model to ignore surface cues and focus on abstract theme, course of action, and outcomes, our system achieved promising performance results. 
%The weaker results of the multi-stage baseline suggest that narrative similarity often depends on subtle causal dependencies that can be diluted by intermediate rewriting or segmentation, supporting an approach that preserves more of the original context during comparison. 
In addition, leveraging the organizers’ annotation guidelines and carefully ordering demonstrations provides a clear benefit. Finally, our findings are consistent with the broader observation that LLMs capture narrative structure more reliably than embedding-based similarity methods.

\section*{\centering Limitations}

\paragraph{Dependence on Proprietary LLMs and Reproducibility}
Our approach relies heavily on commercial, closed-source Large Language Models (e.g., GPT-4o) accessed via paid APIs. This introduces inherent financial costs that scale linearly with the dataset size, potentially limiting the method's deployment in resource-constrained environments. Furthermore, because proprietary models are subject to continuous, unannounced updates by their providers, the long-term reproducibility of our exact experimental results remains a structural limitation. 

\begin{comment}
\paragraph{Probabilistic Inference and Hallucinations}
Despite utilizing aspect-based contrastive reasoning to ground the model's outputs, LLMs remain fundamentally probabilistic. The models can still occasionally suffer from hallucinations—inventing causal links between events that do not exist or falsely aligning narrative distractors. Consequently, there remains a degree of uncertainty in the generated reasoning paths, requiring cautious interpretation when applied to highly ambiguous texts.
\end{comment}

\paragraph{Similarity distractors}
Although the narrative triplet examples were presented to the LLM based on a ranking informed by a difficulty score, which involved, in addition to the narrative aspects, the narrative similarity distractors, the LLM prompt did not give any instructions for these distractors to be used when performing contrastive reasoning. The error analysis shows that these distractors played in important role in many of the observed errors. This makes us conclude that the structured prompt has some limitations, as it should have used definitions and instructions about take in account the similarity distractors as well.

\paragraph{Absence of Task-Specific Fine-Tuning}
Our current methodology operates entirely at inference time via few-shot algorithms. We did not explore supervised fine-tuning (SFT) or parameter-efficient fine-tuning (e.g., LoRA) on open-weight LLMs. Fine-tuning a dedicated model using the dataset's annotated contrastive demonstrations and explicit distractor labels could potentially yield a more robust, cost-effective, and fully reproducible alternative to relying on generalized commercial models.


\bibliography{custom}
\newpage
\appendix




% =========================================================

% Sorted by ascending Difficulty (ties kept in example-number order)
\begin{comment}
    

%\section{Motivating Example}
%%\label{app:aspects}
%\begin{table}[H]
%\centering
%\small
\setlength{\tabcolsep}{6pt}
\renewcommand{\arraystretch}{1.1}
\begin{tabular}{p{0.16\linewidth} p{0.80\linewidth}}
\hline
\textbf{Anchor} & The conservative Brahmin Pathanjali Sasthri severs relations with his modern brother-in-law Bharathi, a doctor. When Sasthri's wife Parvathi, who had two miscarriages, finds herself pregnant, she goes to her brother to get medical aid and incurs the displeasure of her husband. The wife gives birth to a son but simultaneously adopts the son of a lower-caste woman who died in childbirth, creating some confusion for Sasthri as to which baby is his son. Eventually the couple raise the adopted child while their biological son becomes a rickshaw-puller. The two boys grow up and become friends. In the end, the family reunites. \\
\textbf{Story A} & A woman is wrongly accused of a crime that was really committed by her husband and is sent to jail. While in prison she gives birth and the child is put up for adoption. Once fresh evidence frees her from jail, the woman goes searching for her daughter. \\
\textbf{Story B} & A young man, Ben Freidman (Scott Wilson) raised by wealthy adoptive Jewish parents in Beverly Hills, decides that he is ready to finally meet his biological parents. He faces disappointment upon learning of the death of his biological mother. His biological father, meanwhile, resides in a psychiatric care unit. \\
\textbf{Selected} & B \\
\hline
\end{tabular}
\caption{Motivating example for narrative similarity: the most similar candidate is Story B.}
\label{tab:motivating_example}
%\end{table}

As shown in Table~\ref{tab:motivating_example}, picking the most similar story is not just about matching a few shared words like \emph{adoption} or \emph{family}. The anchor is long and mentions many related ideas (pregnancy, adoption, two children, caste, and a family reunion). Story A also has adoption and a mother--child separation, which can look similar on the surface, but its main plot is about wrongful imprisonment. Story B matches the core thread of the anchor more closely: an adopted child later searches for their biological parents and learns difficult truths (death and separation). This kind of example is hard because the key match is spread across many sentences and is mixed with extra details that are not central to the decision.
\end{comment}
%\section{Related Work}
%\label{app:rel}
%The computational modeling of narratives has historically oscillated between rigid symbolic extraction and dense representations. Early text abstraction utilized discrete narrative schemas \cite{chambers2009unsupervised} to capture explicit causal event chains, but their brittleness prevented them from scaling to complex natural language. Modern approaches utilizing dense vector representations like Sentence-BERT \cite{reimers2019sbert} frequently suffer from a strong nominal-participant bias, overly weighting surface-level noun overlap instead of precise thematic functions \cite{nikolaev-pado-2023-representation}.

%Recent research has pivoted toward utilizing LLMs as autonomous evaluators. Frameworks like G-Eval \cite{liu2023geval} demonstrate that generative models achieve higher human correlation when evaluating pairwise relative preferences rather than absolute scores. We extend this using \textbf{aspect-based contrastive reasoning}, drawing on preference optimization principles \cite{rafailov2023direct} to force the model to articulate rationales. Furthermore, while in-context curriculum mechanisms have shown efficacy in general tasks \cite{wang2023curriculum}, our approach uniquely applies a guideline-driven curriculum that strictly orders demonstrations by their reliance on narrative tie-breaking rules \cite{guidelines2026}.

\section{Structured Prompting of the LLM}
\label{app:main_method}

\begin{figure*}[t]
  \centering
    % Replace 'filename' with your image file name (e.g., image.png, image.jpg)
    \includegraphics[width=\linewidth]{images/examples.png}
    \caption{Examples of Narrative Triplets selected for few-shot learning in \textsc{UTD.HLTRI-SIM.Narratives}}
  \label{fig:examples}
\end{figure*}

%\subsection{System Prompt for UTD.HLTRI-SIM.Narratives}
The system prompt used in \textsc{UTD.HLTRI-SIM.Narratives} is a structured prompt designed to guide the LLM in determining which of two narratives (A or B) is more similar to a given Anchor narrative. The prompt first defines narrative aspects that should guide the comparison. It then instructs the model to apply contrastive reasoning by evaluating each aspect for the narrative triplet. Finally, the model is asked to select the narrative that is most similar to the Anchor and provide a final answer accompanied by a brief {\em rationale} that justifies its decision.

\begin{comment}
    

The first component of the prompt consists of the system instructions, which emulate expert human annotation guidelines. Rather than a dense paragraph, these instructions are structured as a precise list of directives designed to calibrate the model's analytical baseline:
\begin{enumerate}[nosep, label=\arabic*.]
    \item \textbf{Definition of the Task:} Explicitly establishes the objective to determine the most narratively similar candidate story to an Anchor story.
    \item \textbf{Definition of Aspects:} Formalizes the definitions for Abstract Theme, Course of Action, and Outcomes, strictly separating them from localized details.
    \item \textbf{Distractors:} Instructs the model to explicitly ignore writing style, concrete settings, character names, and extraneous background noise (D1-D5).
    %\item \textbf{Core Conceptualization:} Guides the model on how to properly conceptualize the stories, such as instructing it to explicitly formulate comprehensive descriptions to capture abstract themes while largely ignoring side storylines.
    \item \textbf{Tie-Breaking Heuristics:} Provides guidance on how to weigh matching aspects, emphasizing that factors should be considered independently and weighed based on an intuitive impression of the comprehensive narrative structure.
     \item \textbf{Instruction on Contrastive Reasoning:} Directs the model to execute contrastive reasoning by comparing each aspect explicitly.
\end{enumerate}
\end{comment}

\begin{lstlisting}[language=plain]
You are an expert annotator specializing in narrative similarity. 
  Your goal is to determine which of two candidate stories (Narrative A or Narrative B) is most similar to an Anchor story based on three core aspects:
  1. Abstract Theme: The defining constellation of problems, central ideas, and core motifs (excluding concrete setting).
  2. Course of Action: The sequence of events, actions, conflicts, turning points, and the order in which they occur.
  3. Outcomes: The results of the plot at the end of the text, for example, the conflict resolution, the characters' fates, moral lessons, etc. It does not cover intermediate statuses that change later in the story.
  For each aspect, apply contrastive reasoning by evaluating the pairs (Anchor, Narrative A) and (Anchor, Narrative B).
  Follow this reasoning process:
  Aspect-wise comparision: For each aspect (Theme, Course of Action, Outcomes), compare how closely Narrative A aligns with the Anchor and how closely Narrative B aligns with the Anchor. Focus on structural and conceptual similarity rather than surface details like names or locations.
  Contrast the candidates: Explicitly contrast the similarity of Narrative A and Narrative B relative to the Anchor for each aspect. Identify which narrative better matches the Anchor on that dimension and why.
  Final Selection: Decide which candidate narrative overall is most similar to the Anchor. Base this decision on the aspects that most strongly align between the narratives.
  Identify contributing aspects: Record the aspects that most influenced the final judgment.
  Final explanation: Summarize the contrastive reasoning and clearly state why the selected narrative is more similar to the Anchor.
\end{lstlisting}



\section{Examples of Narrative Triplets selected for the Few-Shot Learning Scenario}
\label{app:examples}
Figure \ref{fig:examples} shows examples of narrative triplets selected as demonstrations. These examples were chosen to ensure both complexity and diversity. Among the selected triplets, three have only one narrative aspect matched between the most similar narrative and the anchor narrative, three have two narrative aspects matched, and three have all three narrative aspects matched between the most similar narrative and the anchor narrative.




\section{Examples of Demonstrations presented to the \textsc{UTD.HLTRI-SIM.Narratives} system}
\label{app:demo_examples}
Figure \ref{fig:demos} illustrates how the contrastive reasoning process works through concrete demonstrations. Each example presents an {\em Anchor Narrative} alongside {\em Narrative A} and {\em Narrative B}. The annotator then evaluates similarity by systematically comparing the pairs (Anchor, A) and (Anchor, B) across the three defined narrative aspects: abstract theme, course of action, and outcomes.\\
In Example 1, both Narrative A and Narrative B share the same abstract theme as the Anchor, but Narrative A more closely matches the course of actions and final outcome, leading to its selection. In Example 2, the stories differ in theme and outcome, yet Narrative A is still selected because it mirrors the Anchor’s course of action.
\begin{figure*}[ht]
  \centering
    % Replace 'filename' with your image file name (e.g., image.png, image.jpg)
    \includegraphics[width=\linewidth]{images/demos.png}
    \caption{Example of demonstrations used in the \textsc{UTD.HLTRI-SIM.Narratives} system.}
  \label{fig:demos}
\end{figure*}


\section{Error Analysis}
\label{Errors}

\begin{figure*}[ht]
  \centering
    % Replace 'filename' with your image file name (e.g., image.png, image.jpg)
    \includegraphics[width=\linewidth]{images/error_analysis.png}
    \caption{Example of an incorrect LLM prediction in narrative similarity.  CR represents Contrastive Reasoning.}
  \label{fig:error}
\end{figure*}

Figure \ref{fig:error} illustrates a case where the LLM produces an incorrect prediction. The model selects Narrative B as the story most similar to the Anchor, whereas the ground truth indicates that Narrative A is the closer match. The model justifies its choice by claiming that Narrative B aligns with the Anchor in terms of abstract theme, course of action, and outcomes. A closer comparison across these aspects, however, shows that the stronger alignment is actually between the Anchor and Narrative A.\\
The model interprets Narrative B’s focus on duty and protection as thematically similar to the Anchor’s conflict. The Anchor centers on martial arts tradition, the legacy of a master, and the pupils’ responsibility to confront corruption in order to uphold that legacy. Narrative A closely reflects this theme: the story revolves around martial arts training, mentorship, and the protagonist’s struggle to prove herself in a competitive fighting context. The emphasis on martial arts mastery and personal growth mirrors the Anchor’s focus on disciples carrying forward a martial tradition. Narrative B, in contrast, focuses on redemption and protection. Balsa’s mission is to save lives and protect a prince while confronting a political threat. Although this includes elements of duty, it does not revolve around martial arts tradition or preserving a master’s legacy. Thus, the abstract theme aligns more strongly between the Anchor and Narrative A than between the Anchor and Narrative B.\\
The model treats Narrative B’s journey as similar to the Anchor’s progression. In the Anchor, the master dies, the pupils separate, grow through their experiences, and eventually reunite to confront corruption and uphold their master’s legacy. Narrative A follows a related progression centered on martial arts development: Jane is discovered after a confrontation, recruited by a mentor, trained rigorously, and ultimately faces rivals in a martial arts competition. This sequence preserves the training and confrontation structure that characterizes the Anchor. Narrative B, in contrast, follows a different pattern in which a wandering warrior saves a prince, becomes his bodyguard, and undertakes a journey to protect him while uncovering a political threat. This escort-and-protection narrative diverges from the Anchor’s structure of discipleship, growth, and confrontation. As a result, the course of action aligns more closely between the Anchor and Narrative A.\\
Finally, the model treats the presence of a political threat in Narrative B as evidence of a similar ending. In the Anchor, the pupils reunite to confront corrupt authority and defend their master’s legacy, emphasizing martial tradition and collective resistance. Narrative A concludes with Jane confronting her rivals after extensive martial arts training, reflecting personal mastery and conflict resolution within a martial context. Narrative B instead resolves around protecting the prince and addressing a political or mythical threat to the kingdom. This outcome centers on safeguarding a royal figure rather than defending a martial tradition. Therefore, the outcome aligns more closely between the Anchor and Narrative A.\\
Overall, the incorrect prediction occurs because the model overemphasizes Narrative B’s elements of duty and political danger while overlooking stronger structural and thematic similarities between the Anchor and Narrative A. Across abstract theme, course of action, and outcomes, Narrative A more closely matches the Anchor.

\section{Plot-Informed Baseline}
\label{app:plots}

This baseline decomposes the assessment of narrative comparative similarity into three sequential sub-tasks. For the first two sub-tasks the LLM is prompted to generate a transformation of the narrative, while the third sub-task prompt the LLM do decide on the narrative comparative similarity.  
\begin{comment}
(i) \textbf{Normalize} rewrites the narratives into a shared neutral style to reduce surface cues,
(ii) \textbf{Segment} converts each normalized story into chronological plot beats with lightweight metadata,
and (iii) \textbf{Judge} performs aspect-based contrastive comparison (abstract theme, course of action, outcomes) and outputs the final decision.
\end{comment}

\subsection{Sub-task 1: Simplify the narrative}
\begin{comment}
\textbf{Input:} a JSON object with keys \texttt{anchor\_text}, \texttt{text\_a}, \texttt{text\_b} (raw stories). \\
\textbf{Output:} a JSON object with the same keys, each mapped to a normalized story string.
\end{comment}

\begin{lstlisting}[caption={}]
You are a narrative normalizer for a narrative-similarity task.

Task:
Rewrite each input story into a simple, neutral, third-person narrative using a shared style.
Preserve the story's plot-critical meaning, but reduce superficial distractors by:
- Replacing proper names with stable generic roles (e.g., Person1, Person2, Friend, Thief).
- Generalizing specific time/place details to generic terms unless causally necessary.
- Removing sentiment-heavy phrasing; keep plot-relevant facts only.
- Removing background lore/subplots that do not affect the main plot arc.

Hard rules:
- Do NOT invent events. Do NOT remove plot-critical events.
- Keep the rewrite short (about 3-8 sentences).
- Keep chronological order of events.
- Output valid JSON ONLY with keys: anchor_text, text_a, text_b (each a string).
\end{lstlisting}



\subsection{Sub-task 2: Identify plot chronologies}
\begin{comment}
\textbf{Input:} the Stage 1 output JSON (normalized stories). \\
\textbf{Output:} a JSON object with keys \texttt{anchor\_text}, \texttt{text\_a}, \texttt{text\_b}, where each value is a list of 4--8 chronological plot beats. Each beat contains:
\texttt{label}, \texttt{text}, \texttt{function}, \texttt{gist}, \texttt{story\_effect}. The implementation enforces \textbf{exactly one} \texttt{OUTCOME} beat per story.
\end{comment}
\begin{lstlisting}[caption={}]
You are a story segmenter for a narrative-similarity task.

Input:
A JSON object with keys: "anchor_text", "text_a", "text_b". Each value is a normalized story string.

Task:
For EACH story, split the story into 4-8 chronological plot beats (segments) for the MAIN plot arc.
Each beat must be in story-world chronological order.

Beat labels (choose exactly one per beat; use role in the overall arc):
- PREMISE: starting situation / context needed for the arc
- GOAL: what the main character wants/needs (explicit or strongly implied)
- ACTION: a deliberate step taken toward/within the arc
- CONFLICT: an obstacle, pressure, or problem that forces a response
- TURNING_POINT: a key pivot that changes direction or raises stakes
- OUTCOME: the final result/resolution at the end of the story (EXACTLY ONE per story)

For each beat, output an object with fields:
- label: one of {PREMISE, GOAL, ACTION, CONFLICT, TURNING_POINT, OUTCOME}
- text: 1-2 simple sentences describing this beat (faithful to the input story)
- function: a 3-7 word plot-function phrase describing this beat's role
- gist: a compact 5-12 word event summary derived from the beat text
- story_effect: a short phrase describing why this beat matters for progression

Hard rules:
- Do NOT invent facts beyond the normalized story text.
- Preserve chronological order.
- Ensure EXACTLY ONE OUTCOME beat per story.
- Output valid JSON ONLY with keys: anchor_text, text_a, text_b.
  Each value is a list of beat objects as specified above. No extra text.
\end{lstlisting}



\subsection{Sub-task 3: Decide comparative similarity}
\begin{comment}
\textbf{Input:} the Stage 2 output JSON (plot beats for anchor, Story A, Story B). \\
\textbf{Output:} a JSON object containing (i) aspect-wise contrastive reasons, (ii) the selected story (\texttt{A} or \texttt{B}), (iii) contributing aspects, and (iv) a concise final explanation.
\end{comment}
\begin{lstlisting}[caption={}]
You are an expert annotator specializing in narrative similarity.

Goal:
Determine which of two candidate stories (Story A or Story B) is most similar to an Anchor story
based on three core aspects:

1) Abstract Theme: The defining constellation of problems, central ideas, and core motifs
   (excluding concrete setting).
2) Course of Action: The sequence of events, actions, conflicts, turning points, and the order
   in which they occur.
3) Outcomes: The results of the plot at the end of the text (e.g., conflict resolution, characters'
   fates, moral lessons). Outcomes do NOT include intermediate statuses that later change.

Ignore:
- Writing style
- Concrete setting/time period (unless it is required by the events)
- Character and location names
- Text length
- Level of detail in which events are described

Input:
Stage 2 JSON. Each story is a list of chronological beats with labels, gist, function, story_effect.

Instructions:
- Use ONLY the Stage 2 content; do not add new story facts.
- Compare Anchor vs Story A and Anchor vs Story B for EACH aspect independently:
  - For Abstract Theme: infer a concise theme statement for each story from the beats (exclude setting),
    then decide which candidate matches the anchor's theme more closely.
  - For Course of Action: compare the event sequence and key pivots/obstacles, including their order.
  - For Outcomes: compare the OUTCOME beats (final result).
- Then select the overall closer story.

Output JSON ONLY (no extra text) with this schema:
{
  "result": {
    "contra_reason": {
      "theme": "<contrastive theme reasoning>",
      "course": "<contrastive course-of-action reasoning>",
      "outcomes": "<contrastive outcome reasoning>"
    },
    "selected_story": "A" or "B",
    "contributing_aspects": ["A_theme","C_action","Outcomes"],
    "final_explanation": "1-3 concise sentences summarizing why the selected story is closer."
  }
}

Contributing aspects:
Include an aspect in contributing_aspects only if it meaningfully supports the selection.
If an aspect is roughly a tie, omit it.
\end{lstlisting}



\begin{comment}
\subsection{Implementation details}
%The baseline parses each stage output as JSON and validates the schema before continuing.
In particular, Stage 2 enforces that each story contains exactly one \texttt{OUTCOME} beat and that all beat objects include the required string fields (\texttt{label}, \texttt{text}, \texttt{function}, \texttt{gist}, \texttt{story\_effect}).
To improve robustness, the implementation also strips markdown code fences if the model returns JSON inside triple backticks.

\onecolumn
\end{comment}


\end{document}
