Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance

• URL: http://arxiv.org/abs/2403.09085v2
• Date: Mon, 11 Nov 2024 11:35:28 GMT
• Title: Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
• Authors: Kai Xiong, Xiao Ding, Ting Liu, Bing Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Yixin Cao,
• Abstract summary: Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios. LLMs often struggle to abstract and apply the generic fact to provide consistent and precise answers. This has sparked a vigorous debate about whether LLMs are genuinely reasoning or merely memorizing.
• Score: 38.49506722997423
• License:
• Abstract: Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking human-like intelligence. Despite this, when tasked with several simple questions supported by a generic fact, LLMs often struggle to abstract and apply the generic fact to provide consistent and precise answers, revealing a deficiency in abstract reasoning abilities. This has sparked a vigorous debate about whether LLMs are genuinely reasoning or merely memorizing. In light of this, we design a preliminary study to quantify and delve into the abstract reasoning abilities of existing LLMs. Our findings reveal a substantial discrepancy between their general reasoning and abstract reasoning performances. To relieve this problem, we tailor an abstract reasoning dataset (AbsR) together with a meaningful learning paradigm to teach LLMs how to leverage generic facts for reasoning purposes. The results show that our approach not only boosts the general reasoning performance of LLMs but also makes considerable strides towards their capacity for abstract reasoning, moving beyond simple memorization or imitation to a more nuanced understanding and application of generic facts. The code is available at https://github.com/Waste-Wood/MeanLearn.

Related papers

• LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models [52.03659714625452]
  Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks. But, can they really "reason" over the natural language? This question has been receiving significant research attention and many reasoning skills such as commonsense, numerical, and qualitative have been studied.
  arXiv  Detail & Related papers  (2024-04-23T21:08:49Z)
• Do Large Language Models Understand Logic or Just Mimick Context? [14.081178100662163]
  This paper investigates the reasoning capabilities of large language models (LLMs) on two logical reasoning datasets. It is found that LLMs do not truly understand logical rules; rather, in-context learning has simply enhanced the likelihood of these models arriving at the correct answers.
  arXiv  Detail & Related papers  (2024-02-19T12:12:35Z)
• AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation [60.40409210088717]
  Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. We design the framework AbsInstruct to enhance LLMs' abstraction ability through instruction tuning.
  arXiv  Detail & Related papers  (2024-02-16T12:47:11Z)
• Concise and Organized Perception Facilitates Reasoning in Large Language Models [32.71672086718057]
  We show that large language models (LLMs) exhibit failure patterns akin to human-like cognitive biases when dealing with disordered and irrelevant content in reasoning tasks. We propose a novel reasoning approach named Concise and Organized Perception (COP) COP carefully analyzes the given statements to identify the most pertinent information while eliminating redundancy efficiently.
  arXiv  Detail & Related papers  (2023-10-05T04:47:49Z)
• 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)
• Large Language Models Are Not Strong Abstract Reasoners [12.354660792999269]
  Large Language Models have shown tremendous performance on a variety of natural language processing tasks. It is unclear whether LLMs can achieve human-like cognitive capabilities or whether these models are still fundamentally circumscribed. We introduce a new benchmark for evaluating language models beyond memorization on abstract reasoning tasks.
  arXiv  Detail & Related papers  (2023-05-31T04:50:29Z)
• Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners [75.85554779782048]
  Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned textitsemantics of language tokens do the most heavy lifting during the reasoning process.
  arXiv  Detail & Related papers  (2023-05-24T07:33:34Z)
• ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT [72.83383437501577]
  Large language models (LLMs) have recently demonstrated significant potential in mathematical abilities. LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities. This paper presents a novel method for integrating LLMs into the abductive learning framework.
  arXiv  Detail & Related papers  (2023-04-21T16:23:47Z)

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.