Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data

• URL: http://arxiv.org/abs/2409.12437v1
• Date: Thu, 19 Sep 2024 03:39:09 GMT
• Title: Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
• Authors: Jiaming Zhou, Abbas Ghaddar, Ge Zhang, Liheng Ma, Yaochen Hu, Soumyasundar Pal, Mark Coates, Bin Wang, Yingxue Zhang, Jianye Hao,
• Abstract summary: This work explores the potential and limitations of using graph-based synthetic reasoning data as training signals to enhance Large Language Models' reasoning capabilities. Our experiments, conducted on two established natural language reasoning tasks, demonstrate that supervised fine-tuning with synthetic graph-based reasoning data effectively enhances LLMs' reasoning performance without compromising their effectiveness on other standard evaluation benchmarks.
• Score: 53.433309883370974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains. In this work, we explore the potential and limitations of using graph-based synthetic reasoning data as training signals to enhance LLMs' reasoning capabilities. Our extensive experiments, conducted on two established natural language reasoning tasks -- inductive reasoning and spatial reasoning -- demonstrate that supervised fine-tuning (SFT) with synthetic graph-based reasoning data effectively enhances LLMs' reasoning performance without compromising their effectiveness on other standard evaluation benchmarks.

Related papers

• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models [54.04678363287392]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks. Recent advancements in OpenAI o1 and DeepSeek-R1 have further improved performance in System-2 reasoning domains.
  arXiv  Detail & Related papers  (2025-03-20T17:59:38Z)
• LogiDynamics: Unraveling the Dynamics of Logical Inference in Large Language Model Reasoning [49.58786377307728]
  This paper adopts an exploratory approach by introducing a controlled evaluation environment for analogical reasoning. We analyze the comparative dynamics of inductive, abductive, and deductive inference pipelines. We investigate advanced paradigms such as hypothesis selection, verification, and refinement, revealing their potential to scale up logical inference.
  arXiv  Detail & Related papers  (2025-02-16T15:54:53Z)
• Logical Reasoning in Large Language Models: A Survey [17.06712393613964]
  This survey synthesizes recent advancements in logical reasoning in large language models (LLMs) It outlines the scope of logical reasoning in LLMs, its theoretical foundations, and the benchmarks used to evaluate reasoning proficiency. The review concludes with future directions, emphasizing the need for further exploration to strengthen logical reasoning in AI systems.
  arXiv  Detail & Related papers  (2025-02-13T09:19:14Z)
• JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models [51.99046112135311]
  We introduce JustLogic, a synthetically generated deductive reasoning benchmark for rigorous evaluation of Large Language Models. JustLogic is highly complex, capable of generating a diverse range of linguistic patterns, vocabulary, and argument structures. Our experimental results reveal that most state-of-the-art (SOTA) LLMs perform significantly worse than the human average.
  arXiv  Detail & Related papers  (2025-01-24T15:49:10Z)
• Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up [9.42385235462794]
  Large language models (LLMs) have shown remarkable performance in reasoning tasks but face limitations in mathematical and complex logical reasoning. We propose Reversal of Thought (RoT), a novel framework aimed at enhancing the logical reasoning abilities of LLMs. RoT utilizes a Preference-Guided Reverse Reasoning warm-up strategy, which integrates logical symbols for pseudocode planning.
  arXiv  Detail & Related papers  (2024-10-16T07:44:28Z)
• The Role of Deductive and Inductive Reasoning in Large Language Models [35.43513487137371]
  Large Language Models (LLMs) have achieved substantial progress in artificial intelligence, particularly in reasoning tasks. We propose the Deductive and InDuctive(DID) method, which enhances LLM reasoning by dynamically integrating both deductive and inductive reasoning. Our findings suggest that DID provides a more robust and cognitively aligned framework for reasoning in LLMs.
  arXiv  Detail & Related papers  (2024-10-03T18:30:47Z)
• Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing [61.98556945939045]
  We propose a framework to learn planning-based reasoning through Direct Preference Optimization (DPO) on collected trajectories. Our results on challenging logical reasoning benchmarks demonstrate the effectiveness of our learning framework.
  arXiv  Detail & Related papers  (2024-02-01T15:18:33Z)
• LLMs for Relational Reasoning: How Far are We? [8.840750655261251]
  Large language models (LLMs) have revolutionized many areas by achieving state-of-the-art performance on downstream tasks. Recent efforts have demonstrated that the LLMs are poor at solving sequential decision-making problems.
  arXiv  Detail & Related papers  (2024-01-17T08:22:52Z)
• LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models [63.14196038655506]
  We introduce LogicAsker, a novel approach for evaluating and enhancing the logical reasoning capabilities of large language models (LLMs) Our methodology reveals significant gaps in LLMs' learning of logical rules, with identified reasoning failures ranging from 29% to 90% across different models. We leverage these findings to construct targeted demonstration examples and fine-tune data, notably enhancing logical reasoning in models like GPT-4o by up to 5%.
  arXiv  Detail & Related papers  (2024-01-01T13:53:53Z)
• Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
  Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning. We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
  arXiv  Detail & Related papers  (2023-10-02T01:00:50Z)
• GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach [0.0]
  Large Language Models (LLMs) have showcased impressive reasoning capabilities. In this paper, we introduce a novel graph-based method to further augment the reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2023-08-18T03:12:59Z)
• Exploring Self-supervised Logic-enhanced Training for Large Language Models [59.227222647741094]
  In this paper, we make the first attempt to investigate the feasibility of incorporating logical knowledge through self-supervised post-training. We devise an auto-regressive objective variant of MERIt and integrate it with two LLM series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to 13 billion. The results on two challenging logical reasoning benchmarks demonstrate the effectiveness of LogicLLM.
  arXiv  Detail & Related papers  (2023-05-23T06:13:10Z)
• LogiGAN: Learning Logical Reasoning via Adversarial Pre-training [58.11043285534766]
  We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Inspired by the facilitation effect of reflective thinking in human learning, we simulate the learning-thinking process with an adversarial Generator-Verifier architecture. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets.
  arXiv  Detail & Related papers  (2022-05-18T08:46:49Z)

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.