Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models

• URL: http://arxiv.org/abs/2305.16582v2
• Date: Sat, 23 Mar 2024 03:06:54 GMT
• Title: Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models
• Authors: Yao Yao, Zuchao Li, Hai Zhao,
• Abstract summary: We propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. GoT captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. We evaluate GoT's performance on a text-only reasoning task and a multimodal reasoning task.
• Score: 74.40196814292426
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• On the Diagram of Thought [12.304069891580658]
  We introduce Diagram of Thought (DoT), a framework that models iterative reasoning in large language models (LLMs) DoT organizes propositions, critiques, refinements, and verifications into a cohesive DAG structure, allowing the model to explore complex reasoning pathways. We formalize the DoT framework using Topos Theory, providing a mathematical foundation that ensures logical consistency and soundness in the reasoning process.
  arXiv  Detail & Related papers  (2024-09-16T07:01:41Z)
• Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models [63.36637269634553]
  We present a novel method of further improving performance by requiring models to compare multiple reasoning chains. We find that instruction tuning on DCoT datasets boosts the performance of even smaller, and therefore more accessible, language models.
  arXiv  Detail & Related papers  (2024-07-03T15:01:18Z)
• Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning [45.517215214938844]
  Chain-of-thought technique has been received well in multi-modal tasks. We propose a novel Aggregation-Graph-of-Thought (AGoT) mechanism for soft-prompt tuning in multi-modal representation learning.
  arXiv  Detail & Related papers  (2024-04-06T07:39:44Z)
• ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting [124.69672273754144]
  Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of large language models (LLMs) Existing CoT approaches usually focus on simpler reasoning tasks and thus result in low-quality and inconsistent CoT prompts. We introduce CoTGenius, a novel framework designed for the automatic generation of superior CoT prompts.
  arXiv  Detail & Related papers  (2024-03-21T11:34:26Z)
• Graph of Thoughts: Solving Elaborate Problems with Large Language Models [15.711472857775085]
  Graph of Thoughts (GoT) is a framework that advances prompting capabilities in large language models (LLMs) The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph. This work brings the reasoning closer to human thinking or brain mechanisms such as recurrence.
  arXiv  Detail & Related papers  (2023-08-18T17:29:23Z)
• Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals [15.372421458422489]
  Chain-of-Thought (CoT) technique is widely regarded as one of the effective methods for enhancing the reasoning ability of foundation models. This paper proposes a multimodal Hypergraph-of-Thought (HoT) reasoning paradigm, which enables the foundation models to possess the expert-level ability of high-order multi-hop reasoning.
  arXiv  Detail & Related papers  (2023-08-11T16:13:04Z)
• Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker [72.09076317574238]
  ToM is a plug-and-play approach to investigate the belief states of characters in reading comprehension. We show that ToM enhances off-the-shelf neural network theory mind in a zero-order setting while showing robust out-of-distribution performance compared to supervised baselines.
  arXiv  Detail & Related papers  (2023-06-01T17:24:35Z)
• Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings [61.04460792203266]
  We introduce VCoT, a novel method that leverages chain-of-thought prompting with vision-language grounding to bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks.
  arXiv  Detail & Related papers  (2023-05-03T17:58:29Z)
• Multimodal Chain-of-Thought Reasoning in Language Models [94.70184390935661]
  We propose Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework. Experimental results on ScienceQA and A-OKVQA benchmark datasets show the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2023-02-02T07:51:19Z)
• Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango [11.344587937052697]
  This work initiates the preliminary steps towards a deeper understanding of reasoning mechanisms in large language models. Our work centers around querying the model while controlling for all but one of the components in a prompt: symbols, patterns, and text. We posit that text imbues patterns with commonsense knowledge and meaning.
  arXiv  Detail & Related papers  (2022-09-16T02:54:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.