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\title{Latent \textsc{vs} Explicit Knowledge Representation: How ChatGPT Answers Questions about Low-Frequency Entities}

%
\name{Arianna Graciotti$^{\ast}$, Valentina Presutti$^{\ast}$, Rocco Tripodi$^{\dagger}$}
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\address{$^{\ast}$University of Bologna,  $^{\dagger}$Ca’ Foscari University of Venice\\
         $^{\ast}$Via Zamboni 33, 40126, Bologna, Italy, $^{\dagger}$Sestiere Dorsoduro 3246, 30123 Venezia, Italy \\
         $^{\ast}$\{valentina.presutti, arianna.graciotti\}@unibo.it\\
         $^{\dagger}$rocco.tripodi@unive.it\\}
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\abstract{
In this paper, we present an evaluation of two different approaches to the free-form Question Answering (QA) task.
The main difference between the two approaches is that one is based on latent representations of knowledge, and the other uses explicit knowledge representation.
For the evaluation, we developed \textsc{DynaKnowledge}, a new benchmark composed of questions concerning Wikipedia low-frequency entities.
We wanted to ensure, on the one hand, that the questions are answerable and, on the other, that the models can provide information about very specific facts.
The evaluation that we conducted highlights that the proposed benchmark is particularly challenging.
The best model answers correctly only on 50\% of the questions.
Analysing the results, we also found that ChatGPT shows low reliance on low-frequency entity questions, manifesting a popularity bias.
On the other hand, a simpler model based on explicit knowledge is less affected by this bias.
With this paper, we want to provide a living benchmark for open-form QA to test knowledge and latent representation models on a dynamic benchmark.
 \\ \newline \Keywords{Question Answering, Knowledge Representation, LLMs} }

\begin{document}

\maketitleabstract

\section{Introduction}
\label{sec:intro}
The introduction of the transformer architecture \cite{DBLP:conf/nips/VaswaniSPUJGKP17} revolutionised Natural Language Processing (NLP).
It brought a new paradigm in the field with the development of Large Language Models (LLMs), such as BERT \cite{devlin-etal-2019-bert} and GPT \cite{DBLP:conf/nips/BrownMRSKDNSSAA20}, pre-trained on plain text and specialized on defined tasks.
This new paradigm introduced a new way to perform prediction tasks in machine learning, in which instead of modelling directly the probability of an output label $y$ given an input $x$, it provides for the modification of the input $x$ using a template and prompting the LLM to fill it.
The desired output $y$ can be then extracted or inferred from the LLM's response \cite{DBLP:journals/csur/LiuYFJHN23}.
A seminal work in this area has been proposed by \citet{DBLP:journals/jmlr/RaffelSRLNMZLL20}, introducing a unified framework that converts text-based language problems into a text-to-text format.

The task of Question Answering (QA) has also been influenced by this new paradigm.
\citet{petroni-etal-2019-language}, starting from the assumption that LLMs can learn relational knowledge in the same way they learn linguistic knowledge, proposed an approach to extract this knowledge using \emph{fill-in-the-blank} cloze statements.
This line of research paved the way for the development of models that use the latent representations of LLMs as Knowledge Bases (KBs) \cite{petroni-etal-2019-language} and use latent representations and KBs in conjunction to approach knowledge-intensive tasks \cite{DBLP:conf/nips/LewisPPPKGKLYR020,DBLP:conf/icml/GuuLTPC20}.
However, the performance of such models on open-domain QA is still quite low \cite{siriwardhana-etal-2023-improving}.

A related and more general line of research in this context culminated with the development of conversational agents such as ChatGPT \cite{ChatGPT}.
These models can interact with humans and provide answers and solutions to different tasks prompted by the users.
If, from one side, the popularity of this kind of models is increasing constantly, the research community has raised two main concerns to them.
The first one is related to the LLMs' tendency to \emph{hallucinate} \cite{Ji2023} with the consequent generation of biased or harmful content \cite{DBLP:conf/fat/BenderGMS21}.
The second concern regards the evaluation of ChatGPT’s performance due to the closed nature of this model and its continuous updates via Reinforcement Learning from Human Feedback (RLHF) \cite{DBLP:conf/nips/Ouyang0JAWMZASR22} (see Section \ref{sec:ChatGPT}).
If the first bias has social implications, the second may compromise a fair evaluation of the model.
This is because it is not possible to assess if ChatGPT has been contaminated, seeing during one of its training phases evaluation datasets.
This problem has been demonstrated in other recent LLMs \cite{DBLP:conf/uss/CarliniTWJHLRBS21,DBLP:conf/emnlp/DodgeSMAIGM021}.
%\citet{aiyappa-etal-2023-trust} started from the assumption that given the closed nature of ChatGPT it is difficult to evaluate this model. Further, the fact that it is continuously updated using human feedback hinders a proper evaluation and open the hypothesis that it is possible that it has already seen the test datasets of many NLP tasks, as it was attested for other recent LLMs \cite{DBLP:conf/uss/CarliniTWJHLRBS21,DBLP:conf/emnlp/DodgeSMAIGM021}.

To deepen these concerns, we propose a new evaluation based on specialist knowledge.
We developed \textsc{DynaKnowledge}, a new benchmark for QA based on information extracted from Wikipedia and related to non-popular entities in the music domain.
In this way, we can test how reliable ChatGPT is on very specific questions and a dataset that has never been published.
However, it is important to note that the questions in our benchmark are not impossible to answer.
This is because we extracted questions and answers from the English-language Wikipedia, one of the most popular sources in NLP.
We developed this evaluation inspired by the idea that LLMs have difficulties dealing with low-frequency entities and that if prompted with them, they are likely to \emph{hallucinate}, producing fluent but unrealistic text.
We compared the performance of ChatGPT with a knowledge extraction pipeline that uses explicit knowledge representation in the form of Abstract Meaning Representation \cite[AMR]{Banarescu2013} graphs to answer the questions.
This explicit knowledge is structured as a Knowledge Graph \cite[KG]{DBLP:series/synthesis/2021Hogan} and thus can be queried.
Such a structure also provides the source from which the answer was extracted, a desirable feature also for LLMs but not directly derivable from them, given that they are based on latent and not explicit representations.

The contributions of this paper can be listed as follows:
\begin{itemize}
    \item a new dynamic benchmark for open-format QA models;
    \item a new Knowledge Extraction pipeline based on explicit knowledge that can be used for QA;
    %\item a new question answering model based on explicit knowledge;
    \item a systematic comparison between ChatGPT and the explicit knowledge model centred on entity popularity.
\end{itemize}

\noindent \textsc{DynaKnowledge} and the evaluation results can be found at \url{https://github.com/polifonia-project/llms-vs-specialised-knowledge}.

\section{Related Work}
\label{sec:related_work}
In this section, we first introduce recent works on the evaluation of ChatGPT, and then we present recent works about the use of AMR for knowledge extraction and QA.
\subsection{ChatGPT evaluation}
\citet{laskar-etal-2023-systematic} proposed a systematic evaluation of ChatGPT in NLP tasks covering 140 tasks, including QA, text summarization, code generation, and commonsense reasoning, and analyzing 255K responses.
The results of this large-scale evaluation show that ChatGPT can perform a wide variety of tasks with impressive performance.
However, it is still far from achieving good performances in some of them.
A domain-specific evaluation of ChatGPT was proposed by \citet{jahan-etal-2023-evaluation}.
This evaluation centred on the biomedical domain and covered tasks such as relation extraction, document classification, QA, and summarization.
The authors of this work found that ChatGPT in a zero-shot setting can outperform fine-tuned generative models, such as BioGPT \cite{DBLP:journals/bib/LuoSXQZPL22} and BioBART \cite{yuan-etal-2022-biobart}, only when the training set for the task is small.
Otherwise, if the training set for fine-tuning is large, specialized models outperform ChatGPT by a large margin.

A different evaluation aimed at checking how the predictions of ChatGPT change over time was presented by \citet{aiyappa-etal-2023-trust}.
The authors of this work used stance detection as a case in point and followed the experimental setting proposed in previous work by \citet{DBLP:journals/corr/abs-2212-14548}.
In this work publicly available data from SemEval 2016 Task 6 \citelanguageresource{mohammad-etal-2016-semeval,DBLP:journals/toit/MohammadSK17} and P-stance \citelanguageresource{li-etal-2021-p} were used.
The authors prompted ChatGPT using, for each sentence in the dataset, the following template: \textit{what’s the attitude of the sentence:} \textsc{input sentence} \textit{select from “favor, against or neutral”}.
The evaluation shows big improvements comparing results from different time periods, highlighting the difficulty of evaluating this model that is continuously updated with unknown data.
The authors concluded that a fair evaluation of this model should be novel in order to prevent test data leakage.

A common view in the literature concerns the closed nature of this model and the high variability of the results, which change substantially depending on apparently uncorrelated factors, such as the moment in which the model was used or a slight modification of the prompt.
In such conditions, the parameters of the model, such as the temperature that controls the \emph{creativity of the model}, are difficult to tune and adapt to the task at hand.
%\citet{DBLP:journals/corr/abs-2309-08210} evaluated ChatGPT and other open-source LLMs on the challenging task of Long-Form Question Answering (LFQA), showing that ChatGPT is able to produce


\subsection{AMR for Knowledge Extraction and Question Answering}
Graph-based semantic parsing has gained attention due to the potential of general-purpose representations, such as AMR.
Text-to-AMR transduction based on neural machine translation and sequence-to-sequence (\textit{seq2seq}) models achieved high performances (around $85\%$ accuracy) both in English ~\cite{Bevilacqua2021,zhou-etal-2021-structure}, multilingual parsing \cite{blloshmi-etal-2020-xl,cai-etal-2021-multilingual-amr} and multi-formalisms scenarios \cite{Tripodi2021}.

Regarding information extraction end-tasks, \citet{Rao2017} leveraged AMR semantic parsing for extracting information (molecular events/interactions) from biomedical documents, achieving promising results.
With a similar objective (extracting fine-grained information from the biomedical scientific literature), \citet{Zhang2021Biomedical} employed AMR to obtain an in-depth semantic structure of sentences and further enriched the resulting graphs with domain-specific information from external KBs through edge-conditioned graph attention network (GAT, \cite{Velickovic2018GraphAN}).
The fusion of the semantic structures uncovered by the AMR parsing and the highly specialistic knowledge integrated from a domain-focused KB brought tangible advantages for achieving high performances in the end-task goal.

\citet{Kapanipathi2021} satisfactorily employed AMR as an intermediate logic for the semantic parsing of natural language to develop a Knowledge Base Question Answering (KBQA) neuro-symbolic system.
Similarly, \citet{Zhang2021} proved the advantages of employing the rich semantic representation offered by pre-trained AMR parsers to extract entities, relations, and events from unstructured sentences and encapsulate them into Information Networks through an encoder-decoder architecture. 
In \citet{Jungwoo2020}, Commonsense QA is pursued considering the importance of representing the question's meaning to predict the correct answer.
The semantic parsing of the question is obtained by expanding an AMR graph with an external KG, which contains commonsense information (ConceptNet, \citelanguageresource{ConceptNet2017}). The resulting graph is subsequently pruned to retain only the most informative knowledge to accomplish the task, then used to analyse the reasoning path and derive the correct answer. 
%In \citet{bornea2021learning}, the researchers try to overcome the limitations identified in the work presented in \citet{Kapanipathi2021}. 
\citet{Kapanipathi2021} proposes a Neuro-Symbolic Question Answering (NSQA) system, in which AMR parsing is used to understand questions, and the resulting AMR graph is mapped into query graphs (SPARQL queries) aligned with a KB utilising deterministic mappings. \citet{bornea2021learning} considers those deterministic mappings as prone to coverage and granularity mismatch and proposes a framework in which a transition-based semantic parser which integrates a BART-based model learns to transpile AMR into the SPARQL training language.
\citet{ijcai2022p568} employs AMR-based modules (AMR parsing, AMR graph segmentation and AMR-To-Text generation) to better understand complex questions through question decomposition and achieve more interpretable multi-hop QA.

\section{Methodology} %AG_091023 
\label{sec:meth}
In this paper, we want to compare the performance of explicit and latent knowledge models on the QA task.
To this end, we first collected a new benchmark for the task and then selected two representative models for latent and explicit knowledge representation.
The latent representation model is ChatGPT, and the explicit knowledge model is a Knowledge Extraction pipeline whose core component is \cite[Text2AMR2FRED]{Gangemi2023},
a framework based on Abstract Meaning Representation (AMR) \cite{Banarescu2013} that produces graph-based semantic representations of the textual evidence we employed for the QA task.
In the following sections, we describe the models and then the dataset.

\begin{comment}
\begin{table*}[]
%\captionsetup{justification=justified,singlelinecheck=false}
\caption{Coreference resolution and replacements correctness in a sample of sentences extrapolated from Wikipedia.}\label{tab:wiki_coref_evaluation_perc}
\begin{adjustbox}{max width=.75\textwidth}
\begin{tabular}{l r r r}
\midrule 
\multicolumn{1}{c}{mention type} & \multicolumn{1}{c} {\#incorrect}
%{\vtop{\hbox{\strut \#incorrect}\hbox{\strut replacements}}}
& \multicolumn{1}{c} {\#correct}
%{\vtop{\hbox{\strut \#correct}\hbox{\strut replacements}}}
& \multicolumn{1}{c}
{Total}
%{\vtop{\hbox{\strut \#tot. replacements}\hbox{\strut assessed}}}
\\ 
\midrule
third-person personal pronouns & $ 4 (8\%) $ & $ 48 (92\%)$ & $52 (100\%)$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table*}%
\end{comment}

\begin{table*}[]
%\captionsetup{justification=justified,singlelinecheck=false}
\caption{Example of data collection for the named entity corresponding to the Wikipedia page \texttt{Teresina\_Brambilla}, \texttt{Giuseppina\_Ronzi\_de\_Begnis} and \texttt{Wolfgang\_Amadeus\_Mozart}. ID column reports the sample's unique identifier in \textsc{DynaKnowledge} to facilitate the reader in retrieving the sample in the benchmark.}
\label{tab:LLM_comparison_data_collection}
\begin{adjustbox}{max width=\textwidth}
%check resize
\begin{tabular}{lp{1cm}p{5cm}p{3cm}p{8cm}}
\midrule
ID & Gender & Analyst's Question & Analyst's Answer & Sentence from Wikipedia containing the answer\\
\midrule
\texttt{18}&F&How long was Teresina Brambilla's career as a musician?&25 years&Teresa "Teresina" Brambilla (15 April 1845 – 1 July 1921) was an Italian soprano who sang in the major opera houses of Europe in a career spanning 25 years.\\
\texttt{41}&F&Which female colleague did Giuseppina Ronzi de Begnis argue with during the rehearsals of Maria Stuarda?&Anna Del Sere&Ronzi was also known for her capricious attitudes and for having confrontations and arguments with female colleagues, including the famous altercation with Anna Del Sere during the rehearsals of Maria Stuarda.\\
\texttt{5}&M&How old was Wolfgang Amadeus Mozart when he started to compose?&5 years old&Already competent on keyboard and violin, he composed from the age of five and performed before European royalty.\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table*}%


\subsection{Models}
\label{sub:models}
\subsubsection{ChatGPT}\label{sec:ChatGPT}
As far as it is known, ChatGPT is trained in a three-step process \cite{ChatGPT}.
First, an initial LLM based on GPT 3/3.5 \cite{DBLP:conf/nips/Ouyang0JAWMZASR22} is fine-tuned on a dataset created by asking human annotators to write what is the desired output to prompts submitted to the OpenAI API.
The next phase consists of sampling a set of prompts from a larger collection of prompts submitted to the OpenAI API.
For each of them, the LLM produces multiple responses that human annotators then rank.
With this information starts the second training phase in which a reward model is learned on the response-ranking task using the reinforcement learning from human feedback (RLHF) mechanism \cite{DBLP:conf/nips/Ouyang0JAWMZASR22}.
This step keeps the LLM frozen and solely trains the RM.
In the last training step, the LLM generates responses to a new set of prompts that were not included in the previous steps.
In this phase, the now-frozen reward model is used as a reward function, and the LLM is further fine-tuned to maximize this reward using the Proximal Policy Optimization algorithm \cite{DBLP:journals/corr/SchulmanWDRK17}.
%
\begin{figure}
\centering
\caption{The Knowledge Extraction pipeline schema.}
\includegraphics[width=\linewidth]{images/Text2AMR2FRED_largerboxes.png}
\label{fig:text2AMR}
\end{figure}
%
\subsubsection{The Knowledge Extraction pipeline}\label{subsub:ke_framework} %AG_091023
%Text2AMR2FRED\footnote{\url{https://150.146.207.164/txt-amr-fred/}}\label{foot:Text2AMR2FRED_WebApp} 
The knowledge extraction pipeline that we used in this paper relies on Text2AMR2FRED, which exploits AMR parsing to extract knowledge from unstructured text.
AMR formalism is grounded in PropBank's \textit{Frames} \citelanguageresource{Palmer2005}, which constitute the core lexicon of this resource.
Frames consist of predicate-argument structures named \emph{rolesets}.
AMR can be expressed by using the PENMAN serialisation format \cite{Penman1991}, a notation convention that enables encoding the semantic dependencies of directed and rooted graphs such as AMR graphs.
In our framework, depicted in Figure \ref{fig:text2AMR}, AMR graphs serve as an event-centric representation of an input text, well-suited for retrieving the essential elements of a situation described in one or multiple sentences.
As we can see for example in \ref{fig:AMR_comparison}, the \textsc{Arg0} of the PropBank predicate \textsc{sing-01} encodes information about the singer (an \textit{agent} in an act of singing).
The advantage of AMR representation is that it detaches from syntax variability and word forms, providing the same graph for sentences conveying almost the same meaning with a different syntactic or lexical realisation.
Our Knowledge Extraction pipeline extracts information from the text and stores it in AMR-based KGs through its core component, Text2AMR2FRED.
%\paragraph*{Text2AMR.}\label{par:text2amr} We rely on Abstract Meaning Representation (AMR) \cite{Banarescu2013} parsing as an intermediate step for extracting knowledge from unstructured text.  AMR formalism is grounded in PropBank \cite{Palmer2005} \textit{Frames}, which constitute the core lexicon of the PropBank lexical resource. Frames consist of predicate-argument structures named "rolesets". In our framework, AMR graphs serve as an intermediate event-centric representation, well-suited for retrieving the essential elements of a situation described in a text. For example, the \textsc{ARG0} of the PropBank predicate \textsc{sing-01} encodes information about the singer (an \textit{agent} in an act of singing).
%, such as who or what took a particular action and who or what underwent the consequences of a particular event. The advantage of AMR representation is detaching from syntax variability and word forms, providing the same graph for sentences conveying almost the same meaning with a different syntactic or lexical realisation.
\paragraph*{Implementation details.}
In our Knowledge Extraction pipeline, we implement techniques for minimising the loss of information in the source text, such as coreference resolution, i.e., the task of clustering spans of text (\textit{mentions}) that correspond to the same single entity (\textit{referent}).
For our experiments, we used the model proposed by \citet{clark-manning-2016-improving} and applied referent substitutions only to a subset of pronominal mentions, the third-person personal pronouns.
%As we report in Table \ref{tab:wiki_coref_evaluation_perc}
We evaluated the substitutions performed, achieving a $92\%$ accuracy on a set of $52$ third-person pronominal mentions occurring in a sample of sentences extrapolated from Wikipedia.

The text2AMR parsing module of our Knowledge Extraction pipeline relies on a neural semantic parser, i.e., \cite[SPRING]{Bevilacqua2021}, which allows us to perform the AMR2text task with the same model.
This model exploits BLINK \cite{wu-etal-2020-scalable} to link named entities to their unique entry in Wikipedia (\textit{wikification}). %\footnote{For English language documents, we implemented a co-reference resolution pipeline based on Spacy's \textit{neuralcoref} (\url{https://spacy.io/universe/project/neuralcoref})}

As a last step, our Knowledge Extraction pipeline includes \cite[AMR2FRED]{Meloni2017}, a tool that transforms AMR graphs into RDF/OWL KGs that follow \cite[FRED]{Gangemi2017} machine reader's knowledge representation patterns. The resulting KGs are further enriched with knowledge from external Knowledge Bases (KBs) through Framester \cite{Framester2016}, a semantic resources hub. An example of knowledge used to enrich the resulting Knowledge Graphs is Word Sense Disambiguation (WSD) information, which is obtained by submitting the original sentence to \cite[EWISER]{bevilacqua-navigli-2020-breaking} and used to associate WordNet's synsets from the RDF version of WordNet\footnote{\url{https://www.w3.org/TR/wordnet-rdf/}}, included in Framester, to AMR nodes missing links to any external source.

Text2AMR2FRED APIs\footnote{\url{http://framester.istc.cnr.it/txt-amr-fred/api/docs}} enable tools such as the Machine Reading suite\footnote{\url{https://github.com/polifonia-project/machine-reading}} to generate RDF \textit{named graphs}, which allows reporting information about the source of the input text analysed. Text2AMR2FRED is also released to the public via a user-friendly web app\footnote{\url{https://arco.istc.cnr.it/txt-amr-fred/}}.

\subsection{\textsc{DynaKnowledge}: a new Dynamic Benchmark for QA}
\label{sub:dataset}
With this paper, we introduce \textsc{DynaKnowledge}, a new manually curated dataset for open-form QA made of questions based on the Wikipedia pages of historical entities picked from a corpus of music historical periodicals\footnote{This corpus is the \textit{Periodicals} module of the broader Polifonia Textual Corpus (\url{https://github.com/polifonia-project/Polifonia-Corpus}). As we cannot release the module's data for copyright reasons, we release at \url{https://zenodo.org/records/6671912} its metadata containing title, year, and issue number information, allowing for its complete reconstruction.}, whose publication dates range from 1823 to 1900.
The dataset comprises 82 samples (question, answer, textual evidence tuples).
The textual evidence element consists of the Wikipedia page sentences containing the answer to the question. To ensure that our benchmark's questions are not impossible to answer, we included only questions that were unambiguously answerable by reading a passage from English-language Wikipedia. In fact, English-language Wikipedia is commonly included in LLMs training sets, for example, in the GPT-3 training set \cite{DBLP:conf/nips/BrownMRSKDNSSAA20}.

Given the nature of the corpus, the selected historical characters are domain-specific (music) and known to be active or alive before the periodicals' publication dates range. We maintained a 50-50 gender distribution between the selected named entities\footnote{In our study, we restrict to binary gender categories, which, although not reflecting real-world diversity, let us move the first steps towards the definition of our method. We consider a \textit{female} person named entity a historical character that is referred to, in its Wikipedia page, with the pronoun \textit{she}, a \textit{male} person named entity a historical figure who is referred to, in its Wikipedia page, with the pronoun \textit{he}.}. As a result, half of the questions focus on male historical characters and the other half on female historical characters. % Each sample includes a question related to the historical character and its related answer, which is contained in specific sentences on the named entity's Wikipedia page, and eventually included in the samples as textual evidence.
\textsc{DynaKnowledge} is intended to be dynamic and updated over time with new samples always related to low-frequency entities.

\subsubsection{Data collection}
\label{subsub:data_collection}
Data collection was performed by Foreign Languages and Literature undergraduate students who received \textit{ad hoc} training as part of their bachelor's degree curricular internship. They recorded the data in a spreadsheet following the format as in the sample reported in Table \ref{tab:LLM_comparison_data_collection}. On a more nuanced level, the analysts \begin{enumerate*}[label=(\roman*)] \item selected a historical character (technically, a named entity of type \textsc{person}) occurring in a corpus of music historical periodicals; \item accessed the chosen historical character's Wikipedia page and selected one or more sentences reporting a fact relating to the entity; \item formulated a question related to the historical character, answerable by the selected Wikipedia page sentences.\end{enumerate*}

For each sample, we report the following information:
\begin{enumerate}
\item \textsc{ID}: the sample's unique identifier;
\item \textsc{Date}: the date, or range of dates, on which the sample was collected;
\item \textsc{Wikipedia page}: the Wikipedia page of the sample's named entity;
\item \textsc{Gender}: the gender of the sample's named entity;
\item \textsc{Question}: the question about a fact concerning the sample's named entity;
\item \textsc{Prompt}: the prompt submitted to ChatGPT, containing the question;
\item \textsc{Answer}: the ground truth answer;
\item \textsc{Provenance}: the textual evidence, i.e. the sentence(s) from the entity's Wikipedia page containing the answer to the question.
%\item \textsc{MHERCL ID (sent\_n)}: the sentence ID of the MHERCL v0.1 benchmark in which the entity is needed;
%\item \textsc{ChatGPT correct (yes/no)}: manual evaluation as to whether the ChatGPT response is correct or not;
%\item \textsc{ChatGPT Error Type}: attempt to evaluate me on the correctness or otherwise of the ChatGPT answer;
%\item \textsc{ChatGPT answer}: ChatGPT's answer;
%\item \textsc{AMR graph (PENMAN)}: the AMR graph corresponding to the "Sentence (from Wikipedia page) containing the answer";
%\item \textsc{Does the AMR graph contain an answer to the question?}: assessment of the presence or absence of the answer to the question in the AMR graph;
%\item \textsc{If so, which is the sub-graph containing the answer?}: the AMR sub-graph containing the answer to the question;
%\item \textsc{Comment}: if the value reported in \textsc{Does the AMR graph contain an answer to the question?} is "no", we report an explanation of the error observed;
%\item \textsc{QID}: QID corresponding to the sample's named entity Wikipedia page;
%\item \textsc{QID\_frequency}: number of times an internal link in Wikipedia\footnote{We used the enwiki-20220120 dump.} can be mapped to a Wikidata identifier;
\end{enumerate}

The data collection method that we followed for \textsc{DynaKnowledge} is designed to support an ever-evolving benchmark continuously enriched with new examples that focus on less common entities.

%Our benchmark is stored in \texttt{.xlsx} and \texttt{CSV} format in its dedicated GitHub repository\footnote{\url{https://anonymous.4open.science/r/llms-vs-explicit-knowledge-E738}}\label{foot:llms-vs-specialised-knowledge-repo}.
%
\begin{figure}
\caption{AMR subgraph containing the answer to the question \textit{"How long was Teresina Brambilla career as a musician?"}.}
\includegraphics[width=.9\linewidth]{images/amr-graph.png}
\label{fig:AMR_comparison}
\end{figure}
%
\section{Experimental setup} 
\label{sec:experimental_setup}
%AG_091023
%
\begin{figure}
\caption{Process followed by the analysts to record the AMR graph's \emph{answerability} assessment.
If \emph{answerability} is satisfied, the analysts extract the subgraph containing the answer to the question that is then transformed into text via AMR2text to confirm the AMR graph's \emph{answerability}.
If not, they explain the error observed in the graph.}
\includegraphics[width=\linewidth,trim={.8cm .8cm .8cm .8cm},clip]{images/lrec_fig3.pdf}
\label{fig:AMR_answ_ass}
\end{figure}

\begin{table*}[]
%\captionsetup{justification=justified,singlelinecheck=false}
\caption{Evaluation - ChatGPT's answer assessment. ID column reports the sample's unique identifier in \textsc{DynaKnowledge}.}\label{tab:ChatGPT_answer_eval}
\begin{adjustbox}{max width=\textwidth}
%check resize
\begin{tabular}{cp{8cm}p{14cm}p{2cm}}
\midrule
ID & Evaluator's Prompt & ChatGPT's answer & Assessment\\
\midrule
\texttt{18} & How long was Teresina Brambilla's career as a musician? Give a straight answer. & As of my last knowledge update in September 2021, there is no widely known or documented musician by the name of Teresina Brambilla in the public domain. Therefore, I cannot provide information about the length of her musical career. It's possible that she gained recognition after my last update or that she is not a prominent figure in the music world.&Not given\\
\texttt{41} & Which female colleague did Giuseppina Ronzi de Begnis argue with during the rehearsals of Maria Stuarda? Give a straight answer. & Giuseppina Ronzi de Begnis argued with Maria Malibran during the rehearsals of Maria Stuarda. & No \\
\texttt{5} & How old was Wolfgang Amadeus Mozart when he started to compose? Give a straight answer. & Wolfgang Amadeus Mozart started composing at the age of 5. & Yes \\
\midrule
\end{tabular}
\end{adjustbox}
\end{table*}%

\begin{comment}
\begin{table*}[]
\captionsetup{justification=justified,singlelinecheck=false}
\caption{Evaluation - AMR graph's answerability}\label{tab:AMR_comparison}
\begin{adjustbox}{max width=\textwidth}
%check resize
\begin{tabular}{p{20cm}p{1,3cm}}
\midrule
AMR subgraph containing the answer& Correct?\\
\midrule
\raisebox{-0.5\height}{\includegraphics[width=\linewidth]{images/tbrambilla_amrgraph.png}}
&Yes\\
\midrule
\end{tabular}
\end{adjustbox}
\end{table*}%
\end{comment}
%Our experimental setup pursues the objective of comparing ChatGPT's answers to the questions formulated and recorded in the data collection phase (cfr. \ref{sub:data_collection}) to the ability of the AMR graph generated from the Wikipedia sentences collected in the same phase via our Knowledge Extraction framework to answer the same question. We define this AMR graph's ability as \emph{answerability}.
%Text2AMR2FRED\footref{foot:Text2AMR2FRED_WebApp} 
%and GPT-3.5 (Default)\footnote{\url{https://chat.openai.com/}}\label{foot:ChatGPT}.
%We framed the comparison experiment as a QA task.
We tested GPT-3.5 (Default) model, accessed via its free online version (ChatGPT\footnote{\url{https://chat.openai.com/}} and our Knowledge Extraction pipeline (cfr. \ref{subsub:ke_framework}).
We designed and executed the experiments from August 4th to August 22nd, 2023.
At that time, it was not possible for a user to fine-tune GPT-3.5 (Default) model.
Therefore, we evaluated it in a zero-shot setting.
We tested GPT-3.5 (Default) and our Knowledge Extraction pipeline on the same questions formulated and collected in the data collection phase (cfr. \ref{subsub:data_collection}).
We compared the free text answers to the questions returned by ChatGPT and the AMR graphs produced by parsing, with our Knowledge Extraction pipeline, the Wikipedia sentences containing the answer to the question. %However, we allowed users to interact with it more than one time if its first answer is not correct or not inherent to the question.
%The primary goal of our experiment was to manually curate a new dataset for question answering, derived from information extracted from Wikipedia and related to historical characters. Both the data collection and the evaluation phases of our experiment were conducted manually by human analysts. 

%\subsection{Data collection}
%\label{sub:data_collection}
%\subsubsection{Macro-step 1}\label{par:1st_macrostep}The first macro-step is described in the numbered list below:

%As a first step, the analysts involved in the experiment execution had to collect the required data. Data collection was performed by University of Bologna's Foreign Languages and Literature undergraduate students who received \textit{ad hoc} training as part of their bachelor's degree curricular internship. They recorded the experiment's results in a spreadsheet following the format as in the sample reported in Table \ref{tab:LLM_comparison_data_collection}. On a more nuanced level, the analysts \begin{enumerate*}[label=(\roman*)] \item selected a historical character (technically, a named entity of type \textsc{person}) occurring in a corpus of music historical periodicals; \item accessed the chosen historical character's Wikipedia page and selected one or more sentences reporting a fact of the named entity's life; \item formulated a question related to the historical character, answerable by the selected Wikipedia page sentences.\end{enumerate*}

\subsection{Evaluation}\label{sub:eval}
%For the evaluation, the analysts had to compare GPT3.5's free-text answers to the questions formulated and collected in the data collection phase and the answerability of the AMR graphs created by applying Text2AMR parsing to the Wikipedia sentences containing the answer.
To perform the evaluation, the analysts had to compare ChatGPT's answers to the questions and AMR graph's \emph{answerability}.
The evaluation was performed by the same interns who conducted the data collection, with the support of the authors of this paper for the AMR graph \emph{answerability} assessment.

\subsubsection{ChatGPT answer}\label{subsub:ChatGPT_answ}
To assess ChatGPT's answer, the analysts had to submit the questions as prompts to the free online version of ChatGPT and manually assess the answer returned by ChatGPT.
They recorded the results by adding the following information to the dataset described at \ref{sub:dataset}:

\begin{enumerate}
\item \textsc{ChatGPT answer}: ChatGPT's answer;
\item \textsc{ChatGPT answer assessment (yes/no/not given)}: manual evaluation of whether the ChatGPT response is correct (\textit{"yes"}), incorrect (\textit{"no"}), or the model does not give any answers (\textit{"not given"}).
\end{enumerate}

Given the reduced size of our sample, we manually compared ChatGPT's and ground truth answers to ensure the high quality of the assessment. Such a choice was also made in light of the awareness raised by \citet{chen-etal-2019-evaluating} and \citet{roberts-etal-2020-much} on how QA metrics based on tokens overlap can hinder the evaluation of questions that require more complex and abstractive answers. %Also Roberts et al., 2020, "How Much Knowledge Can You Pack into the Parameters of a Language Model?" carries out human evaluation to avoid a negative assessment of answers generated by the model that does not match the gold answer because of different phrasing, given the open-form nature of the task. 

%\paragraph*{Example.}\label{par:ChatGPT_answ_ex}In the example reported in Table \ref{tab:ChatGPT_answer_eval}, ChatGPT's answer has been assessed as \textit{not given}. In fact, ChatGPT replies to the prompt stating that, to the best of its knowledge, there is no known musician named \textit{Teresina Brambilla}, and for this reason, it cannot answer the question asked (namely, returning information about the length of her career).

\subsubsection{AMR graph}\label{subsub:amr_answ}
We manually checked the AMR graphs to assess AMR graphs' \emph{answerability} to guarantee the highest assessment quality, as stressed out by \citet{ettinger-etal-2023-expert}. The process is depicted in Figure \ref{fig:AMR_answ_ass}.
The analysts transformed the Wikipedia sentences containing the answer to the question into an AMR graph via Text2AMR2FRED and then assessed the \emph{answerability} of the AMR graph.
We define \emph{answerability} as the ability of the AMR graph to encode the information needed to answer a question. 
It is satisfied when the AMR graph contains a subgraph that correctly captures the information needed to answer a question.
The subgraph must report the semantically correct PropBank predicates, their core roles (arguments), and non-core roles (relations) structure.
It must be possible to translate the subgraph back to text using an AMR2text model 
%\footnote{The AMR2text parsing can be performed via the SPRING web service available at \url{http://nlp.uniroma1.it/spring/}.} 
and obtain a natural language sentence containing an answer to the question.
%The AMR2text task can be performed via the same AMR parser employed in our Knowledge Extraction framework (SPRING, cfr. \ref{subsub:ke_framework}).
They recorded the AMR graphs' answerability assessment results by adding the following information to the dataset described at \ref{sub:dataset}:

\begin{enumerate}
\item \textsc{AMR graph (PENMAN)}: the AMR graph corresponding to the \textsc{Provenance} (cfr. \ref{subsub:data_collection});
\item \textsc{AMR graph's answerability (yes/no/not given)}: manual assessment of the \emph{answerability} of the AMR graph;
\item \textsc{If so, which is the sub-graph containing the answer?}: the AMR sub-graph containing the answer to the question;
\item \textsc{AMR graph's error description}: if the value reported in \textsc{Does the AMR graph contain an answer to the question?} is "no", an explanation of the error observed.
%\item \textsc{QID}: QID corresponding to the sample's named entity Wikipedia page;
%\item \textsc{QID\_frequency}: number of times an internal link in Wikipedia\footnote{We used the enwiki-20220120 dump.} can be mapped to a Wikidata identifier;
\end{enumerate}

%\begin{comment}
\subsubsection{Metrics}
We started from the assumption that finding the most suitable metrics for QA is a difficult task and that a metric such as BLEU \cite{papineni-etal-2002-bleu}, ROUGE \cite{lin-2004-rouge} or METEOR \cite{banerjee-lavie-2005-meteor} can alter the evaluation of free-form QA \cite{chen-etal-2019-evaluating}.
With this in mind, as we presented in the previous section, we manually checked the answers of the two models under analysis to ensure that the results were not biased.
With the answers manually checked and divided into the canonical \textit{correct}, \textit{wrong}, and \textit{not given}, we are able to compute standard precision (P), recall (R) and F1 (F1) as: 
\begin{equation*}
P=\frac{correct}{|samples|}\textrm{, } R=\frac{correct}{|samples|-\textrm{not given}},
\end{equation*}
\noindent and
\begin{equation*}
    F1=\frac{2*P*R}{P+R}.
\end{equation*}
%The experiment breaks down into two macro-steps, described in the paragraphs below. 

\begin{comment}
%\section{The Dataset}\label{sub:dataset} %AG_091023
\begin{table*}
%\captionsetup{justification=justified,singlelinecheck=false}
\caption{AMR graphs' answerability VS ChatGPT's answers correctness - Assessment Comparison}
\label{tab:AMR2FRED_vs_ChatGPT_compar}
\centering
\begin{adjustbox}{width=.75\textwidth}
\begin{tabular}{c|cc|ccc}
\toprule
\multirow{2}{*}{\centering \vtop{\hbox{NE's}\hbox{\strut gender}}} & \multicolumn{2}{c}{\centering \vtop{\hbox{\strut Does the \textbf{AMR graph} contain}\hbox{\strut an answer to the question?}}} & \multicolumn{2}{c}{\centering \vtop{\hbox{\strut Is \textbf{ChatGPT's}}\hbox{\strut answer correct?}}} \\
 &  \#yes & \#no & \#yes & \#no \\
 \midrule
F & $\mathbf{21 (51\%)}$ & $20 (49\%)$ & $9 (22\%)$ & $32 (78\%)$\\
M & $20 (49\%)$ & $21 (51\%) $ & $\mathbf{22 (54\%)}$ & $19 (46\%)$\\
\midrule
Total & $\mathbf{41 (50\%)}$ & $41 (50\%)$ & $31 (38\%)$ & $51 (62\%)$\\
\midrule
\midrule
\multicolumn{5}{c}{QID average frequency comparison}&Grand total\\
\midrule
F & $41,714$ & $31,526$ & $\mathbf{76,222}$ & $25,451$ & $36,875$\\
M & $817,263 $ & $341,555$ & $\mathbf{910,333}$ & $180,111$ &$573,307$\\
\midrule
Grand total & $410,1$ & $190,512$ & $\mathbf{660,1}$ & $82,265$ & $301,696$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table*}
\end{comment}
%With this paper, we contribute by introducing a new manually curated challenging dataset for question answering. The dataset is made of 82 samples.
%Each sample revolves around a named entity of type \textsc{person} picked from a corpus of music historical periodicals, whose publication dates range from 1823 to 1900. Human evaluators were instructed to maintain a 50-50 gender distribution between the selected male and female named entities. As a result, half of the questions focused on male historical characters and the other half on female historical characters. Given the nature of the corpus, the selected historical characters are related to music and known to be active or alive in a period similar to the periodicals' publication dates range. 

\section{Results and Analysis} %AG_091023: Tabella
\label{sec:res_disc}
Table \ref{tab:AMR2FRED_vs_ChatGPT_compar} compares ChatGPT answers and the AMR graphs' answerability assessment.
The table shows that the AMR graphs effectively capture the answers to the questions $50\%$ of the times, while ChatGPT's answer is correct $38\%$ of the times (precision).
We can also see that the recall of ChatGPT is higher than precision.
ChatGPT, in fact, can give an answer that indicates that it is not able to provide information.
On the other hand, this is not possible with AMR parsing. We considered a question prompted to AMR as \textit{not given} only if the parser cannot produce a graph.
This happened just one time in our experiments.

\begin{table}
%\captionsetup{justification=justified,singlelinecheck=false}
\caption{ChatGPT's answers and AMR graphs' answerability assessment reported as precision (P), recall (R), f1-measure (F1) dividing the questions according to the gender of the named entity involved, female (F) and male (M).}
\label{tab:AMR2FRED_vs_ChatGPT_compar}
\centering
\begin{adjustbox}{width=.9\linewidth}
\begin{tabular}{l|rrr|rrr}
\toprule
\multirow{2}{*}{\centering \vtop{\hbox{NE's}\hbox{\strut gender}}} & \multicolumn{3}{c}{\centering ChatGPT} & \multicolumn{3}{c}{\centering AMR} \\
 &  P & R & F1 & P & R & F1 \\
 \midrule
F & $0,22$ & $0,31$ & $0,26$ & $0,51$ & $0,51$ & $0,51$\\
M &  $0,54$ & $0,58$ & $0,56$ & $0,49$ & $0,50$ & $0,49$\\
\midrule
Total & $0,38$ & $0,46$ & $0,42$ & $\mathbf{0,50}$ & $\mathbf{0,50}$ & $\mathbf{0,50}$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}

\begin{table*}
%\captionsetup{justification=justified,singlelinecheck=false}
\caption{The Wikidata identifier (QID) average frequency and standard deviation distribution across the named entities of our sample, split per gender and ChatGPT answers'/AMR graphs' answerability assessment}
\label{tab:AMR2FRED_vs_ChatGPT_avg_stdev_freq_compar_freq}
\centering
\begin{adjustbox}{width=\textwidth}
\begin{tabular}{l|rrrrrr|rrrrrr|rr}
\toprule
\multirow{3}{*}{\centering \vtop{\hbox{NE's}\hbox{\strut gender}}} & \multicolumn{6}{c}{\centering ChatGPT answer} & \multicolumn{6}{c}{\centering AMR graph answerability} \\
 &  \multicolumn{2}{c}{\centering\#yes} & \multicolumn{2}{c}{\centering\#no} & \multicolumn{2}{c}{\centering\#not given} & \multicolumn{2}{c}{\centering\#yes} & \multicolumn{2}{c}{\centering\#no} & \multicolumn{2}{c}{\centering\#not given} & \multicolumn{2}{c}{\centering\#Total}\\
&Avg & StdDev & Avg & StdDev & Avg & StdDev & Avg & StdDev & Avg & StdDev & Avg & StdDev & Avg & StdDev\\ 
\midrule
F & $76$ & $126$ & $37$ & $93$ & $4$ & $3$ & $42$ & $96$ & $30$ & $83,5$ & \texttt{N/A} & \texttt{N/A} & $36$ & $89$\\
M & $869$ & $1579$ & $201$ & $600$ & $6$ & $3$ & $776$ & $1627$ & $338.5$ & $739$ & $61$ & \texttt{N/A} & $545$ & $1253$\\
\midrule
All & $639$ & $1372$ & $110$ & $407$ & $4$ & $3$ & $400$ & $1183$ & $184$ & $542$ & $61$ & \texttt{N/A} & $291$ & $919$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table*}

\begin{table}
\caption{Correlation between the gender/popularity of the named entities of our sample and ChatGPT's answers/AMR graphs' answerability assessment reported as Spearman's Rho coefficient ($\rho_s$).}
\label{tab:AMR2FRED_vs_ChatGPT_SpearmanRhoCoeff}
\centering
\begin{adjustbox}{width=\linewidth}
\begin{tabular}{l|cc} % Change 'rr' to 'cc' for centering
\toprule

{\multirow{2}{*}{\centering \vtop{\hbox{}\hbox{NE's}\hbox{\strut feature}}}} & \multicolumn{2}{c}{Spearman correlation} \\
 &  {\centering \vtop{\hbox{ChatGPT's}\hbox{\strut answer assessment}}} & \vtop{\hbox{AMR graph's}\hbox{\strut answerability}} \\
 \midrule
Popularity & $0.48$ & $-0.07$ \\
Gender &  $0.33$ & $-0.02$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}


%\subsection{Examples}
%\subsubsection{ChatGPT answer correctness} 

In the example with ID \texttt{18} reported in Table \ref{tab:ChatGPT_answer_eval}, ChatGPT's answer has been assessed as \textit{not given}. In fact, ChatGPT replies to the prompt stating that, to the best of its knowledge, there is no known musician named \textit{Teresina Brambilla}, and for this reason, it cannot answer the question asked (\textit{"How long was Teresina Brambilla's career as a musician?"}). But, as recorded in Table \ref{tab:LLM_comparison_data_collection}, the sentence from Wikipedia containing the answer (\textsc{Provenance}) collected for this sample states that the correct answer for the sample's question is \textit{25 years}.
In the example with ID \texttt{41} reported in Table \ref{tab:ChatGPT_answer_eval}, ChatGPT's answer assessment is \textit{no}, meaning that the answer given by ChatGPT is not correct. In fact, as recorded in Table \ref{tab:LLM_comparison_data_collection}, the sentence from Wikipedia containing the answer (\textsc{Provenance}) collected for this sample reports that the correct answer for the sample's question (\textit{"Which female colleague did Giuseppina Ronzi de Begnis argue with during the rehearsals of Maria Stuarda?"}) is \textit{Anna Del Sere}, while ChatGPT mentions an argument between Giuseppina Ronzi and Maria Malibran.
In the example with ID \texttt{5} reported in Table \ref{tab:ChatGPT_answer_eval}, ChatGPT's answer assessment is \textit{yes}. ChatGPT's answer correctness is verifiable by comparing ChatGPT's answer in Table \ref{tab:ChatGPT_answer_eval} with the analyst's answer reported in Table \ref{tab:LLM_comparison_data_collection}: both contain the same piece of knowledge, namely that Mozart started composing at the age of five.

%\subsubsection{AMR graph answerability}\label{par:amr_answ_ex}
Figure \ref{fig:AMR_comparison} reports the AMR subgraph containing the answer to the question formulated for the sample with ID \texttt{18} (cfr. Table \ref{tab:LLM_comparison_data_collection}). The subgraph, extrapolated from the AMR graph resulting from the text2AMR parsing of the \textsc{Provenance} (sentence from Wikipedia containing the answer) of the same sample is considered able to answer the question. It has a \emph{root node}, \textsc{z0/soprano}, that is linked to the \emph{instance node} \textsc{z1/person} by the \emph{non-core role} \textsc{:domain}. This triple correctly reflects the \emph{"named-entity-is-noun"} semantics. 
The \emph{instance node} \textsc{z1/person} is linked to the \emph{instance node} \textsc{z2/name} by the \emph{non-core role} \textsc{:name}. This triple correctly states that the \emph{instance node} \textsc{z1/person} has a \textit{name}, therefore is a \textit{named entity}. It branches out in three leaves nodes linked by the relation \textsc{:op}, which reports the three tokens of the sentence that make up the named entity mention (\textit{Teresa}, \textit{"Teresina"}, \textit{Brambilla}).
The \emph{instance node} \textsc{z1/person} is also linked to the PropBank \emph{predicate} node \textsc{z8/sing-01} by the \emph{inverse core role} \textsc{:ARG0-of}. This triple correctly captures the semantics of relative clauses. 
\textsc{sing-01} is also linked to the node \textsc{z14/career} by the \emph{non-core role} \textsc{:duration}, which is itself linked through the same relation to the \emph{instance node} \emph{z15/temporal-quantity}, which captures the semantics of a specific type of quantity. It branches out into the two tokens of the sentence that make up the temporal quantity mention by its arguments \textsc{:unit} (\textit{z16/year}) and \textsc{:quant} (\textit{25}).

The semantic structure of this subgraph encloses all the elements required to answer the question asked, as can also be verified by transforming the AMR graph back to text employing the same model used for Text2AMR, i.e., SPRING. %\footnote{The AMR2text parsing is performed via the SPRING web service available at \url{http://nlp.uniroma1.it/spring/}.} which, following a back-translation approach \cite{sennrich-etal-2016-improving}, 
The resulting natural language sentence \textit{Teresa Teresina Brambilla is a soprano who has sung for a 25-year career} answers correctly the question asked (cfr. the column \texttt{Analyst's Answer} in Table \ref{tab:ChatGPT_answer_eval}).

In the AMR graph resulting from the text2AMR transformation of the sentence from Wikipedia containing the answer (\textsc{Provenance}) of the sample with ID \texttt{41}, the named entity \textit{Maria Stuarda} is incorrectly classified as a \textsc{person} instead of as a \textsc{work-of-art}:

\begin{quote}
\begin{verbatim}
(z16 / person
    :name (z17 / name
    :op1 "Maria"
    :op2 "Stuarda"))
\end{verbatim}
\end{quote}

The named entity classification reported in the example above compromises the correctness of the resulting AMR graph. The AMR graph \emph{answerability} criteria is not satisfied.

\subsection{Issues}\label{par:amr_answ_issues} While assessing AMR graphs' \emph{answerability}, we encountered two main issues.

\paragraph{Coreference resolution} Some of the sentences collected from Wikipedia pages (cfr. \ref{subsub:data_collection}) contain only mentions of the entity and not the entity's referent.
Mentions can be \emph{personal pronouns} (as in the sentence \textit{"He composed from a very young age, first studying with his uncle Giovanni Mazzetti and later with Luigi Caruso in Perugia"}), \emph{possessive pronouns} (as in the sentence \textit{"His parents were John Reeves, a musician of Yorkshire origin, and his wife, Rosina"}), %\emph{nominals} (as in the sentence \textit{"Barnby was born at York, as a son of Thomas Barnby, who was an organist"}), or 
\textit{names} (i.e., proper nouns in their shortened forms, as in the sentence \textit{"Barnby was born at York, as a son of Thomas Barnby, who was an organist"}). %In the course of our work on our Knowledge Extraction framework (cfr. sub-section \ref{subsub:ke_framework}), we dealt with the co-reference resolution problem by applying substitutions to only a subset of pronominal mentions (the third-person personal pronouns). As we report in Table \ref{tab:wiki_coref_evaluation_perc}, we evaluated the substitutions performed, achieving a 92\% accuracy on a set of $52$ on pronominal mentions of the kind occurring in a sample of sentences extrapolated from Wikipedia. 
As reported in paragraph \ref{subsub:ke_framework}, in our Knowledge Extraction pipeline, we apply coreference resolution to only a subset of pronominal mentions (the third-person personal pronouns). As a consequence, AMR subgraphs extrapolated from sentences in which the only mention of the entity is a possessive pronoun or a surname are never assessed as correct because, at present, we do not manage them in our Knowledge Extraction framework. Instead, we considered AMR subgraphs extrapolated from sentences in which the only reference to the entity was a third-person personal pronoun as correct when they corresponded to our \emph{answerability} criteria. 

\paragraph{Wikification}
In assessing AMR graphs \emph{answerability}, we do not assess the correctness of the entity linking information.
Still, we focus on the correct recognition of the entity mention and on its correct classification to a pre-defined entity type\footnote{Entity types list (\url{https://www.isi.edu/~ulf/amr/lib/ne-types.html}) and criteria for their assignation are defined in AMR annotation guidance instructions.}.
This is because, in our particular case, we are using Wikipedia documents, from which it is possible to extract unambiguous entity references by disposing of entity linking altogether.

\subsection{Popularity and gender effect} Looking at the results in Table \ref{tab:AMR2FRED_vs_ChatGPT_compar} in a more granular way, we notice that ChatGPT's answers tend to be wrong more often when questions regard female historical characters: ChatGPT's answer is correct $22\%$ of the times when prompted with questions regarding female named entities, and $54\%$ of the times when prompted with questions regarding male named entities. These observations might corroborate the hypotheses of gender bias in LLMs \cite{cheng-etal-2023-marked} and in KBs such as Wikipedia \cite{stranisci-etal-2023-wikibio}.

Also, we wanted to verify whether the named entities' popularity influenced the results, as other research suggests \cite{chen-etal-2021-evaluating} \cite{pmlr-v202-kandpal23a}. We define \textit{popularity} as each named entity's Wikidata identifier (QID) frequency of occurrence as an internal link in Wikipedia\footnote{We used the \texttt{enwiki-20220120} dump.}. We added the following two pieces of information to the benchmark described in \ref{sub:dataset}:

\begin{enumerate}
\item \textsc{QID}: The QID corresponding to the sample's named entity Wikipedia page;
\item \textsc{Popularity (QID\_frequency)}: number of times an internal link in Wikipedia can be mapped to a QID.
\end{enumerate}

As we can see in Table \ref{tab:AMR2FRED_vs_ChatGPT_avg_stdev_freq_compar_freq}, male named entities have, in our dataset, an average QID frequency of $545$ against $36$ of the female named entities.
Suppose we focus on the sub-sample of cases in which ChatGPT returns the highest number of correct answers: those cases correspond to male historical characters with the highest QID frequency average across the whole sample ($869$).  %These observations might corroborate the hypotheses of gender bias in LLMs \cite{cheng-etal-2023-marked} and in KBs such as Wikipedia \cite{stranisci-etal-2023-wikibio}, which in our dataset seem to be amplified by popularity bias.

To strengthen our hypothesis, we calculate the correlation between ChatGPT's answers' assessment and named entities' popularity using Spearman's Rho coefficient ($\rho_s$). As we can see in Table \ref{tab:AMR2FRED_vs_ChatGPT_SpearmanRhoCoeff}, the result obtained for popularity ($\rho_s$ = $0.48$) show a positive correlation. We repeat the experiment for AMR graphs' answerability, and the results obtained ($\rho_s$ = $-0.07$) demonstrate no correlation. When considering gender as a feature, we obtain the same results: a positive correlation with ChatGPT's answers' assessment ($\rho_s$ = $0.33$), no correlation ($\rho_s$ = $-0.02$) with AMR graphs' answerability.

Notably, the AMR graphs' answerability is apparently robust to any major bias toward the gender or popularity of the entities involved. With regard to ChatGPT's answers' assessment, the popularity bias seems to amplify the gender bias: we eventually obtain wrong answers more often when our questions regard female named entities, which in our sample also have the lowest average popularity. This confirms our hypothesis that a simpler knowledge extraction pipeline is more robust to variation in named entities' features, such as popularity and gender, than an LLM such as ChatGPT.


\section{Future work}
\label{par:LLMs_compar_future_work}
In future work, we plan to expand our benchmark by adding more samples.
The way in which we want to develop the benchmark is dynamic, aligning with current trends in benchmarking NLP models \cite{kiela-etal-2021-dynabench}.
We believe that it is necessary since models like ChatGPT are continuously updated, and an answer from these models can vary over time or with slight changes to the prompt.
This will allow us to examine further the interplay between gender and popularity biases and to deepen the investigation regarding the difficulties that LLMs demonstrate when dealing with long-tail knowledge \cite{pmlr-v202-kandpal23a}.

We also plan to transform the AMR graphs output by our Knowledge Extraction pipeline into Resource Description Framework (RDF) KGs, as Text2AMR2FRED allows. This will enable us to automate the evaluation through structured interrogations and develop an automatic evaluation metric.

Eventually, we aim to combine the capabilities of both the latent and explicit knowledge representation models in a single framework in line with retrieval-augmented generative models \cite{DBLP:conf/nips/LewisPPPKGKLYR020} and testing the resulting model on the same benchmark. Following \citet{mallen-etal-2023-trust}, we want to assess whether using the explicit knowledge produced by our knowledge extraction pipeline to apply constraints on LLM's output will enhance the results and reduce hallucination, mitigating gender and popularity bias on domain-specific and long-tail entities as the ones in \textsc{DynaKnowledge}.


\section{Conclusion}\label{sec:concl}
In this paper, we conducted an evaluation of explicit and latent knowledge representation models.
This evaluation was possible thanks to the development and release of \textsc{DynaKnowledge}, a new benchmark for the open-form QA task. The samples included in \textsc{DynaKnowledge} revolve around named entities of type person extrapolated from historical periodicals. The benchmark's questions are all answerable by reading a passage from Wikipedia. 
This first release serves as a starting point for developing a dynamic benchmark that will be updated over time to tackle the continuous improvements that current LLMs are having.
The results of our experiments show that our benchmark is particularly challenging.
In particular, we found that ChatGPT struggles to answer questions related to less popular entities. %We define popularity as the frequency of a named entity's QID appearing as an internal link in Wikipedia.
This result was accompanied by the assessment of the higher performances of a simple knowledge extraction pipeline.
Such a pipeline demonstrated to be more robust on the variation of named entities' features such as gender and popularity. %, around which the questions of \textsc{DynaKnowledge} have been formulated.

\section{Limitations}

In this paper, we want to provide a feasibility study for developing a fully automatic model for open-domain QA founded on AMR-based KGs. On the one hand, we wanted to test whether our knowledge extraction pipeline correctly encoded the information required to answer the questions, storing it in KGs that can be reliably queried to obtain the answers. On the other hand, we shed light on the sub-optimal performance of ChatGPT when elicited to output factual knowledge about less popular named entities. We concentrated on ChatGPT among all the LLMs because of its widespread popularity and commoditization. The capability of our knowledge extraction pipeline to provide explicit knowledge that can be automatically queried to obtain reliable answers or that can augment ChatGPT performance on open domain QA over long-tail entities should be tested in a fully automatised scenario, in which subgraphs satisfying the answerability condition are automatically retrieved. 


\section{Acknowledgements}


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  The authors gratefully acknowledge the support of the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101004746.
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%\section{Limitations}


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