LLMs for Relational Reasoning: How Far are We?

• Published in: 2024 IEEE/ACM International Workshop on Large Language Models for Code (LLM4Code) pp. 119 - 126
• Main Authors: Li, Zhiming, Cao, Yushi, Xu, Xiufeng, Jiang, Junzhe, Liu, Xu, Teo, Yon Shin, Lin, Shang-Wei, Liu, Yang
• Format: Conference Proceeding
• Language: English
• Published: ACM 20.04.2024
• Subjects: Benchmark testing; Cognition; Large language models; Logic; Pipelines; Planning; Program Induction; Reinforcement learning; Relational Reasoning; Software engineering; Surges
• DOI: 10.1145/3643795.3648387

Large language models (LLMs) have revolutionized many areas (e.g. natural language processing, software engineering, etc.) by achieving state-of-the-art performance on extensive downstream tasks. Aiming to achieve robust and general artificial intelligence, there has been a surge of interest in investigating the reasoning ability of the LLMs. Whereas the textual and numerical reasoning benchmarks adopted by previous works are rather shallow and simple, it is hard to conclude that the LLMs possess strong reasoning ability by merely achieving positive results on these benchmarks. Recent efforts have demonstrated that the LLMs are poor at solving sequential decision-making problems that require common-sense planning by evaluating their performance on the reinforcement learning benchmarks. In this work, we conduct an in-depth assessment of several state-of-the-art LLMs' reasoning ability based on the inductive logic programming (ILP) benchmark, which is broadly recognized as a representative and challenging measurement for evaluating logic program induction/synthesis systems as it requires inducing strict cause-effect logic to achieve robust deduction on independent and identically distributed (IID) and out-of-distribution (OOD) test samples. Our evaluations illustrate that compared with the neural program induction systems which are much smaller in model size, the state-of-the-art LLMs are much poorer in terms of reasoning ability by achieving much lower performance and generalization using either natural language prompting or truth-value matrix prompting. 1