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        <title>GoT: Effective Graph-of-Thought Reasoning in Language Models</title>
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        <namePart type="given">Yao</namePart>
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        <namePart type="given">Zuchao</namePart>
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    <name type="personal">
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            <namePart type="given">Kevin</namePart>
            <namePart type="family">Duh</namePart>
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            <namePart type="given">Helena</namePart>
            <namePart type="family">Gomez</namePart>
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    <abstract>With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT’s performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (ScienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT on the ScienceQA test set. Our code is publicly available at https://github.com/Zoeyyao27/Graph-of-Thought</abstract>
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%0 Conference Proceedings
%T GoT: Effective Graph-of-Thought Reasoning in Language Models
%A Yao, Yao
%A Li, Zuchao
%A Zhao, Hai
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F yao-etal-2024-got
%X With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT’s performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (ScienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT on the ScienceQA test set. Our code is publicly available at https://github.com/Zoeyyao27/Graph-of-Thought
%U https://aclanthology.org/2024.findings-naacl.183
%P 2901-2921

Markdown (Informal)

[GoT: Effective Graph-of-Thought Reasoning in Language Models](https://aclanthology.org/2024.findings-naacl.183) (Yao et al., Findings 2024)

• GoT: Effective Graph-of-Thought Reasoning in Language Models (Yao et al., Findings 2024)

ACL

• Yao Yao, Zuchao Li, and Hai Zhao. 2024. GoT: Effective Graph-of-Thought Reasoning in Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2901–2921, Mexico City, Mexico. Association for Computational Linguistics.