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\title{AmazUtah\_NLP at SemEval-2024 Task 9: A MultiChoice Question Answering System for Commonsense Defying Reasoning}

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\author{Mina Ghashami\thanks{Both authors contributed equally to this work.} \\
  Amazon Web Services \\
  \texttt{ghashami@amazon.com} \\\And
  Soumya Smruti Mishra\footnotemark[1] \\
  Amazon Web Services \\
  \texttt{soumish@amazon.com} \\}

\begin{document}
\maketitle
\begin{abstract}
The SemEval 2024 BRAINTEASER task represents a pioneering venture in Natural Language Processing (NLP) by focusing on lateral thinking, a dimension of cognitive reasoning that is often overlooked in traditional linguistic analyses. This challenge comprises of Sentence Puzzle and Word Puzzle subtasks and aims to test language models' capacity for divergent thinking. 

In this paper, we present our approach to the BRAINTEASER task. We employ a holistic strategy by leveraging cutting-edge pre-trained models in multiple choice architecture, and diversify the training data with Sentence and Word Puzzle datasets. To gain further improvement, we fine-tuned the model with synthetic humor/jokes dataset and the RiddleSense dataset which helped augmenting the model's lateral thinking abilities. Empirical results show that our approach achieve 92.5\% accuracy in Sentence Puzzle subtask and 80.2\% accuracy in Word Puzzle subtask. 
\end{abstract}

\section{Introduction}

The success of language models has inspired the Natural Language Processing community to attend to tasks that require implicit and complex reasoning. Human reasoning encompasses two types of reasoning: lateral and vertical thinking approaches. Lateral thinking demand out-of-the-box thinking. It is a form of creative reasoning that deviates from traditional, logical processes and has received little attention from NLP community. Vertical thinking on the other hand, relies on logical reasoning, and have been relatively popular in the past few years. 

The BRAINTEASER dataset by \citep{jiang-etal-2023-brainteaser} stands as a crucial benchmark for evaluating question-answering systems. It particularly assesses these systems on their ability for lateral thinking — pushing them to transcend conventional commonsense reasoning towards more innovative and creative approaches to problem-solving. Additionally, as part of this effort to test language models' lateral thinking capabilities, the SemEval 2024 BRAINTEASER task \citep{jiang-ilievski-ma:2024:SemEval2024}, offers a focused challenge derived from the broader dataset, further probing the creative reasoning abilities of these models. This task is crucial because it addresses a gap in Natural Language Processing (NLP) where most tasks focus on linear, logical (vertical) thinking, neglecting the complex, divergent aspects of human cognition represented by lateral thinking. By encompassing two subtasks — Sentence Puzzle and Word Puzzle — BRAINTEASER aims to test a model's ability to go beyond conventional commonsense associations, requiring an understanding of both standard meanings and the ability to reinterpret them in novel ways. This is vital for advancing the field of NLP, as it pushes the boundaries of what artificial intelligence can achieve in terms of mimicking the nuanced and creative aspects of human thought.

Our system adopts a multifaceted strategy for this challenge, centering on the use of advanced pre-trained models like BERT \citep{devlin2019bert} and DeBERTaV3 \citep{he2023debertav3} through HuggingFace's \citep{wolf2020huggingfaces} AutoModelForMultipleChoice and AutoModelForSequenceClassification. This approach is enhanced by a diverse training regimen that mixes Sentence and Word Puzzle datasets, ensuring a broad exposure to different types of lateral thinking challenges. Additionally, the model is fine-tuned with a humor/jokes dataset generated by GPT-4 \citep{openai2024gpt4} and the RiddleSense \citep{lin-etal-2021-riddlesense} dataset, which introduces elements of creativity, unconventional thinking, and complex puzzle-solving. This comprehensive strategy aims to equip the model with enhanced lateral thinking abilities, crucial for tackling the creative and nuanced demands of the BRAINTEASER task in SemEval 2024.

In our participation in the BRAINTEASER task, we discovered that our system, particularly when finetuned with AutoModelForMultipleChoice, outperformed the baseline instruction-tuned systems mentioned in the original paper. This approach demonstrated a significant advantage in handling multiple-choice tasks. However, we faced challenges with AutoModelForSequenceClassification, suggesting an area for improvement. The incorporation of additional synthetic data and open-source dataset like RiddleSense positively influenced our performance. Quantitatively, our system achieved a commendable 6th place in the Sentence Puzzle and 10th in the Word Puzzle, indicating stronger proficiency in sentence-based challenges and room for growth in word-based puzzles. 

Our code and data will be available at \url{https://github.com/soumyasmruti/semeval-2024-brainteaser} after cleaning and de-anonimization.

\section{Background}
The task involves two types of brain teasers: Sentence Puzzle and Word Puzzle. In Sentence Puzzle, the input is a sentence-based question that defies commonsense, with multiple-choice answers. For instance, "A man shaves everyday, yet keeps his beard long." The choices include "He is a barber," "He wants to maintain his appearance," and so on. The Word Puzzle involves a word-based teaser, like "What part of London is in France?" with choices focusing on letters in the words (e.g., "The letter N"). The output in both cases is the selection of the correct choice that represents lateral thinking.

In order to counter the potential for Large Language Models (LLMs) memorizing solutions, BRAINTEASER \citep{jiang-etal-2023-brainteaser} incorporates two novel methods of puzzle generation: semantic and context reconstruction. These techniques generate variations of puzzles that preserve the core challenge of overturning conventional commonsense reasoning without altering the fundamental nature of the puzzles. This approach is aimed at enhancing the robustness of the puzzles against the memorization capabilities of LLMs, ensuring that the puzzles continue to effectively test the models' ability to engage in lateral thinking by challenging ingrained commonsense assumptions. This is to ensure the model is evaluating reasoning ability rather than memorization.

Systems are evaluated based on two accuracy metrics: Instance-based Accuracy, considering each question (original and adversarial) as a separate instance, and Group-based Accuracy, where a system must correctly solve all questions in a group (original and its adversarial versions) to score.

\section{Related Work}
We can broadly categorize the reasoning landscape of language models into two groups. The first, is `commonsense reasoning`, also known as `vertical reasoning`. This refers to the ability to make deductions based on everyday knowledge. 
The second category is `lateral reasoning`; i.e. a creative problem-solving approach that involves looking at situations from unconventional perspectives. 

Researchers have explored various approaches to endow LLMs with commonsense reasoning abilities \citep{rae2021scaling}. One prominent approach is the use of knowledge graphs, which represent structured knowledge in the form of entities and their relationships \citep{ilievski2021cskg}. Authors in \citep{wang2021inductive} proposed a method for incorporating commonsense knowledge from ConceptNet \citep{speer2018conceptnet} into language models, leading to improved performance on commonsense reasoning tasks.

Another approach involves fine-tuning pre-trained LLMs on commonsense reasoning datasets. Authors in this paper \citep{huang2019cosmos} introduced the COSMOS QA dataset, which consists of multiple-choice questions that require commonsense reasoning. They showed that fine-tuning pre-trained LLMs on this dataset can significantly improve their commonsense reasoning capabilities.

Researchers have also investigated the use of prompting techniques to elicit commonsense reasoning from LLMs without explicit fine-tuning. \citep{zhou2022conditional} proposed a method called "Conditional Prompt-Tuning" that enables LLMs to perform commonsense reasoning by conditioning on carefully designed prompts. In another work \citep{wei2022chain}, chain-of-thought prompting showed how to unlock LLM's reasoning ability via effective prompting techniques. 

There hasn't been extensive research on `lateral thinking` of LLMs. Very recenlty, OlaGPT \citep{xie2023olagpt} proposed a cognitive architecture framework in which they summarize various methods of human reasoning into Chain-of-Thought (CoT) templates, to maximize the LLMs’ reasoning effect. 

Overall, while LLMs have shown flashes of non-linear, exploratory thinking on some benchmarks, lateral thinking as a holistic cognitive process remains an open challenge.

\section{Methodology}
In this section, we describe different methods and approaches we employed in solving the BrainTeaser puzzle.

%    \subsection{Zero shot with GPT 4}
%    In an innovative zero-shot approach, we presented GPT-4 with /questions and answer choices extracted directly from the test dataset, without resorting to any training data or model fine-tuning. This methodology allowed us to tap into GPT-4's intrinsic understanding of language and context. By circumventing the need for explicit training or model adaptation, we leveraged GPT-4's pre-existing knowledge and capabilities to navigate through the question-answer pairs in the test data and accurately select the correct answers. 
  %/

  

\subsection{Sequence Classification with BERT}
In this approach, we enhanced the performance of a sequence classification model through the instruction fine-tuning process. We leveraged the powerful contextual embeddings provided by BERT \citep{devlin2019bert}. Our methodology involved initializing the model with pre-trained BERT weights and employing the streamlined `AutoModelForSequenceClassification` class from the Hugging Face Transformers library, which linearly projects the embedding from
the language model encoder to each document
into the class logits for that document. We instructed the model with selecting the most appropriate answer from a set of four choices provided alongside a given question. Despite the meticulous fine-tuning process our experimental results revealed sub-optimal performance. 
    

\subsection{MultipleChoice QA with BERT and DeBERTa}
We leveraged the versatile `AutoModelForMultipleChoice` architecture from Hugging Face's library, which integrates a pre-trained transformer model with a specialized classification head. This architecture was pivotal in adapting the model for our multiple-choice task, which involved combining both Word Puzzle and Sentence Puzzle datasets to diversify our training data.

To ensure optimal performance, we split our training data into separate training and validation sets. Throughout the training process, we utilized the validation set to fine-tune hyperparameters, ensuring the model's efficacy.

The AutoModelForMultipleChoice architecture comprises a pre-trained base transformer augmented with a classification head. This head, typically consisting of neural network components such as linear layers and activation functions, enables the model to make informed multiple-choice predictions.

Our model initialization involved embedding pre-trained DeBERTa representations, followed by further training on the designated training dataset. This approach facilitated the model's adaptation to our specific task requirements, ultimately enhancing its performance.

\subsubsection{Augmenting with RiddleSence and Humor Data}
Next, we decided to use two additional data sources to augment our training data. This was with the aim of expanding the diversity of our dataset, enriching it with a wide range of humor styles, scenarios, and perspectives. This augmentation not only increases the robustness and variety of our model but also enhances its adaptability to different contexts. We utilized the public Riddlesense dataset as well as creating humor style data by prompting GPT 4. 

The Ridlesense dataset consists of Riddles which are a form of puzzle where a question, often presented in a cryptic or metaphorical manner, challenges the reader to find a clever or unexpected answer. 

To create the humor style QA, we prompted GPT 4. Crafting jokes content often requires a touch of ingenuity, an out-of-the-box approach, and a healthy dose of lateral thinking, and GPT-4 allowed us to explore unconventional and amusing angles to questions and answers. It's like having a comedy writer who never runs out of fresh and unexpected punchlines. The details about how the dataset was generate is provided in Appendix ~\ref{sec:humordataset}.

We then used the same AutoModelForMultipleChoice architecture and trained the model on augmented training data. 

\section{Experimental setup}

\subsection{Datasets Description}

\begin{table*}[bt!]
\centering 
\begin{tabular}{lcccccccccccc} \toprule
& \multicolumn{3}{c}{Sentence Puzzle} & \multicolumn{3}{c}{Word Puzzle} \\ \cmidrule(lr){2-4}\cmidrule(lr){5-7}
{\bf Dataset} & {\bf Train} & {\bf Validation} & {\bf Test} & {\bf Train} & {\bf Validation} & {\bf Test} \\
\midrule
Provided & 405 & 102 & 120 & 316 & 80 & 96 &  \\
Humor Data GPT4 & 211 & - & - & 211 & - & - &  \\
Riddlesense & 4531 & - & - & - & - & - &  \\
\bottomrule
\end{tabular}
\caption{Dataset Statistics, `-` means the data was not used for the stage of the task. }
\label{tab:dataset}
\end{table*}

The task dataset and additional datasets used in our approaches are detailed in Table ~\ref{tab:dataset}, with all datasets being in the English language. We did not perform any extra pre-processing on the original training or test data. To generate humor data, we used GPT-4 \citep{openai2024gpt4} using prompt engineering. Regarding the RiddleSense \citep{lin-etal-2021-riddlesense} dataset, which originally had five labels, we adapted it to a four-label format. This was achieved by reassigning questions with the fifth label as the correct answer to the fourth choice. Consequently, all fifth-choice answers across questions were remapped to their corresponding fourth choices, and all original fifth choices were discarded. Riddlesense and humor datasets, were selected for their similarity to the original training data, offering commonsense-defying puzzles. For details on the train-validation-test split, please refer to Table~\ref{tab:dataset}. We also experimented by adding SWAG \citep{zellers2018swag} and CODAH \citep{chen2019codah} datasets, but found that they reduced overall performance.

\subsection{Implementation Details}
The raw text was tokenized using a byte-level Byte-Pair Encoding (BPE) vocabulary with 50,257 merge rules, and inputs longer than 1024 tokens were truncated.

Our models were based on the BERT-base and DeBERTaV3 base architectures. The BERT model comprises 12 layers, 768-dimensional embeddings, and 12 attention heads, totaling 117M parameters. The DeBERTaV3 base model features 12 layers and a hidden size of 768, with 110M backbone parameters and a 128K token vocabulary introducing an additional 98M parameters in the embedding layer.

Both models were initialized with pre-trained weights in the AutoModelForMultipleChoice architecture. We conducted a random hyperparameter search, exploring batch sizes of [4, 16, 32] and learning rates of [5e-5, 1e-4, 2e-4]. The configurations yielding the highest validation accuracy were selected for each model size.

We utilized Amazon SageMaker for training, opting for the ml.p3.8xlarge instance for BERT-based approaches and the ml.p3.16xlarge instance for training our DeBERTaV3-based approaches. The training time for the BERT models with the original data was under 20 minutes, while the DeBERTa-based approaches were trained in under one hour. This efficient use of resources enabled us to achieve significant performance improvements with minimal cost and time. 

\section{Results}

\begin{table*}[t]
\centering \small
\begin{tabular}{lcccccccccccc} \toprule
& \multicolumn{6}{c}{Sentence Puzzle} & \multicolumn{6}{c}{Word Puzzle} \\ \cmidrule(lr){2-7}\cmidrule(lr){8-13}
{\bf Approaches} & \rotatebox{90}{\bf Original} & \rotatebox{90}{\bf Semantic} & \rotatebox{90}{\bf Context} & \rotatebox{90}{\bf Orig. + Sem.} & \rotatebox{90}{\bf Orig. + Sem. + Con.} & \rotatebox{90}{\bf Overall} & \rotatebox{90}{\bf Original} & \rotatebox{90}{\bf Semantic} & \rotatebox{90}{\bf Context} & \rotatebox{90}{\bf Orig. + Sem.} & \rotatebox{90}{\bf Orig. + Sem. + Con.} & \rotatebox{90}{\bf Overall} \\
\midrule
\textcolor{gray}{\bf Human} & \textcolor{gray}{.907} & \textcolor{gray}{.907} & \textcolor{gray}{944} & \textcolor{gray}{.907} & \textcolor{gray}{.889} & \textcolor{gray}{.920} & \textcolor{gray}{.917} & \textcolor{gray}{.917} & \textcolor{gray}{.917} & \textcolor{gray}{.917} & \textcolor{gray}{.900} & \textcolor{gray}{.917} \\
\midrule
\textcolor{gray}{ChatGPT} & \textcolor{gray}{.608} & \textcolor{gray}{.593} & \textcolor{gray}{.679} & \textcolor{gray}{.507} & \textcolor{gray}{.397} & \textcolor{gray}{.627} & \textcolor{gray}{.561} & \textcolor{gray}{.524} & \textcolor{gray}{.518} & \textcolor{gray}{.439} & \textcolor{gray}{.292} & \textcolor{gray}{.535} \\
\midrule
\textcolor{gray}{RoBERTa-L} & \textcolor{gray}{.435} & \textcolor{gray}{.402} & \textcolor{gray}{.464} & \textcolor{gray}{.330} & \textcolor{gray}{.201} & \textcolor{gray}{.434} & \textcolor{gray}{.195} & \textcolor{gray}{.195} & \textcolor{gray}{.232} & \textcolor{gray}{.146} & \textcolor{gray}{.061} & \textcolor{gray}{.207} \\
\midrule
\makecell[l]{BERT-base +\\AMSC +\\train-data-wp+sp} & .475 & .55 & .5 & .35 & .25 & .508 & .281 & .312 & .375 & .031 & 0 & .323 \\
\midrule
\makecell[l]{BERT-base +\\AMMC +\\train-data-wp+sp} & .650 & .625 & .625 & .600 & .500 & .600 & .438 & .375 & .406 & .344 & .375 & .406 \\
\midrule
\makecell[l]{DeBERTaV3 +\\AMMC +\\train-data-wp+sp} & .900 & .900 & .850 & .900 & .825 & .883 & .75 & .75 & .625 & .719 & .500 & .708 \\
\midrule
\makecell[l]{DeBERTaV3 +\\AMMC +\\train-data-wp+sp +\\ Humor + RiddleSense} & \bf .925 & \bf .950 & \bf .900 & \bf .925 & \bf .875 & \bf .925 & - & - & - & - & - & - \\
\midrule
\makecell[l]{DeBERTaV3 +\\AMMC +\\train-data-wp +\\ Humor}& - & - & - & - & - & - & \bf .844 & \bf .812 & \bf .750 & \bf .781 & \bf .594 & \bf .802 \\
\bottomrule
\end{tabular}
\caption{SemEval2024 Task 9: BRAINTEASER results table, which shows the performance of different approaches on the test set. Orig. = Original, Sem. = Semantic, Con. = Context, AMSC = AutoModelForSequenceClassification, AMMC = AutoModelForMultipleChoice }
\label{tab:results}
\end{table*}

In Table 2, we demonstrate the performance of our model, where the provided numbers represent the accuracy for various groups. "Original," "Semantic," and "Context" denote the original question, its semantic reconstruction, and context reconstruction, respectively. These three categories are based on instance-based accuracy, where each question is treated as a separate instance. The score reports the accuracy for both the original question and its adversarial counterparts. "Orig. + Sem." represents group-based accuracy, where the original question and its semantic reconstruction are considered and calculated together. Similarly, "Orig. + Sem. + Con." includes the previous group along with the contextual reconstruction of the original question.

In the table, "AMSC" represents AutoModelForSequenceClassification, and "AMMC" represents AutoModelForMultipleChoice. The models used are bert-base-uncased and microsoft/deberta-v3-base. The notation "train-data-wp+sp" indicates that the training data for this approach includes both sentence puzzle and word puzzle training data provided by the organizers of the task. "Humor" represents the synthetic dataset generated by prompting GPT-4, and "RiddleSense" refers to the open-source RiddleSense dataset \citep{lin-etal-2021-riddlesense}. The scores of human performance and the baseline system, as provided in the original paper \citep{jiang-etal-2023-brainteaser}, are depicted in gray. Scores obtained by our system are shown in black, with the best performances for each task highlighted in bold.

\subsection{Subtask A : Sentence Puzzle}
Initially, we trained our models only on the provided sentence puzzle dataset but soon realized that combining both the sentence puzzle and word puzzle datasets yielded better validation scores. Consequently, we used the bert-base model with AutoModelForSequenceClassification, achieving an overall accuracy of 50.8\%. Given that the dataset is in a multiple-choice format, we experimented with AutoModelForMultipleChoice using the same bert model. This change significantly improved performance, increasing accuracy by 10 points to 60\%. Encouraged by this, we opted for the larger DeBERTaV3 model under the AutoModelForMultipleChoice configuration. This model, combined with the original dataset, significantly boosted performance, raising overall accuracy to 83.3\%. After incorporating additional datasets containing humor-style questions and the RiddleSense dataset, our best accuracy score reached 92.5\%. Our approach ranked 6th among the 31 teams that participated in the task and outperformed the baseline zero shot ChatGPT by almost 50 percentage points. 

\subsection{Subtask B : Word Puzzle}
The word puzzle setup followed almost the same approach as sentence puzzle but during validation process we found the best model was the one which was trained with only original training data from word puzzle dataset and adding humor dataset.
Adding RiddleSense data and sentence pzzle data didn't improve the score of the word puzzle in validation process, therefore we didn't submit that output. Our approach for this subtask didn't perform that well when compared to other teams, we ranked 10th among the 23 teams that participated in this task, but outperformed the baseline zero shot ChatGPT by almost 40 percentage points. 

\section{Conclusion}

In this work, we present our novel system designed for the SemEval 2024 BRAINTEASER task, which notably achieved 6th place in the Sentence Puzzle and 10th in the Word Puzzle categories. Our approach leverages advanced pre-trained models like BERT and DeBERTa, optimized through HuggingFace's AutoModelForMultipleChoice and AutoModelForSequenceClassification. This strategy was further enhanced by incorporating a diverse training regimen, blending Sentence and Word Puzzle datasets with a unique humor/jokes dataset and the RiddleSense dataset. This mix has been instrumental in equipping our model with the lateral thinking capabilities essential for this task. While our system excelled in the Sentence Puzzle, reflecting a stronger grasp in sentence-based lateral thinking, the performance in the Word Puzzle highlighted areas for improvement, particularly in word-based lateral reasoning. The additional challenge posed by adversarial versions of puzzles, involving both Semantic and Context Reconstruction, underscores the complexity of this task. Our system's performance underscores the efficacy of our training approach in enhancing lateral thinking in language models, a significant step forward in NLP. Future work will focus on refining our approach for word-based puzzles and further enhancing the model's ability to navigate complex, creative reasoning paths, thereby advancing the field's understanding of AI's potential in mimicking nuanced aspects of human cognition.

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\appendix

\section{Humor Dataset Details}
\label{sec:humordataset}
We used the following prompts to generate Jokes or Humor style dataset. We experimented with multiple prompts and gather all the output in a json file and analyzed them manually. 

PROMPT 1 - Could you create a dataset for me that includes humor-styled questions, each with multiple choices and an answer? The dataset should be in JSON format.

PROMPT 2 - Could you create a dataset of 40 jokes for me in JSON format? Each joke should include four options and the correct answer.

PROMPT 3 - Could you generate an additional 20 jokes with multiple choices and an answer? Please ensure there are no duplicates and that none of them are the same as those previously generated.

\end{quote}

We initially prompted GPT 4 to generate 200 questions at once but that didn't go well. The output contained duplicate questions after 15 / 16 unique ones. Basically, the model kept repeating itself. So we mostly used PROMPT 3 multiple times to generate high quality data. Before adding each we checked for duplicates again manually.  Provided below are some of the jokes generated by the prompt.

\begin{quote}
    {
        "joke": "Why did the bicycle fall over?",
        "options": [
            "A. Because it was two-tired.",
            "B. It had a flat.",
            "C. It was unbalanced.",
            "D. It slipped."
        ],
        "answer": "A"
    }
    
    {
        "joke": "What's orange and sounds like a parrot?",
        "options": [
            "A. A carrot",
            "B. An orange bird",
            "C. A tangerine",
            "D. A flamingo"
        ],
        "answer": "A"
    },
\end{quote}
\end{document}
