Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models
- URL: http://arxiv.org/abs/2406.17169v1
- Date: Mon, 24 Jun 2024 23:02:56 GMT
- Title: Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models
- Authors: Nisarg Patel, Mohith Kulkarni, Mihir Parmar, Aashna Budhiraja, Mutsumi Nakamura, Neeraj Varshney, Chitta Baral,
- Abstract summary: Multi-LogiEval is a comprehensive evaluation dataset encompassing multi-step logical reasoning with various inference rules and depths.
We conduct evaluations on a range of Large Language Models including GPT-4, ChatGPT, Gemini-Pro, Yi, Orca, and Mistral.
- Score: 46.26140720993383
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As Large Language Models (LLMs) continue to exhibit remarkable performance in natural language understanding tasks, there is a crucial need to measure their ability for human-like multi-step logical reasoning. Existing logical reasoning evaluation benchmarks often focus primarily on simplistic single-step or multi-step reasoning with a limited set of inference rules. Furthermore, the lack of datasets for evaluating non-monotonic reasoning represents a crucial gap since it aligns more closely with human-like reasoning. To address these limitations, we propose Multi-LogiEval, a comprehensive evaluation dataset encompassing multi-step logical reasoning with various inference rules and depths. Multi-LogiEval covers three logic types--propositional, first-order, and non-monotonic--consisting of more than 30 inference rules and more than 60 of their combinations with various depths. Leveraging this dataset, we conduct evaluations on a range of LLMs including GPT-4, ChatGPT, Gemini-Pro, Yi, Orca, and Mistral, employing a zero-shot chain-of-thought. Experimental results show that there is a significant drop in the performance of LLMs as the reasoning steps/depth increases (average accuracy of ~68% at depth-1 to ~43% at depth-5). We further conduct a thorough investigation of reasoning chains generated by LLMs which reveals several important findings. We believe that Multi-LogiEval facilitates future research for evaluating and enhancing the logical reasoning ability of LLMs. Data is available at https://github.com/Mihir3009/Multi-LogiEval.
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) - A & B == B & A: Triggering Logical Reasoning Failures in Large Language
Models [65.86149763739141]
We introduce LogicAsker, an automatic approach that comprehensively evaluates and improves the logical reasoning abilities of LLMs.
We evaluate LogicAsker on six widely deployed LLMs, including GPT-3, ChatGPT, GPT-4, Bard, Vicuna, and Guanaco.
The results show that test cases from LogicAsker can find logical reasoning failures in different LLMs with a rate of 25% - 94%.
arXiv Detail & Related papers (2024-01-01T13:53:53Z) - InfiMM-Eval: Complex Open-Ended Reasoning Evaluation For Multi-Modal
Large Language Models [50.03163753638256]
Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence.
Our benchmark comprises three key reasoning categories: deductive, abductive, and analogical reasoning.
We evaluate a selection of representative MLLMs using this rigorously developed open-ended multi-step elaborate reasoning benchmark.
arXiv Detail & Related papers (2023-11-20T07:06:31Z) - 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) - Are Large Language Models Really Good Logical Reasoners? A Comprehensive
Evaluation and Beyond [32.797832207443896]
Large Language Models (LLMs) have emerged as a noteworthy innovation in natural language processing (NLP)
We aim to bridge this gap and provide comprehensive evaluations in this paper.
arXiv Detail & Related papers (2023-06-16T13:39:35Z) - 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)
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.