Lost in the Logic: An Evaluation of Large Language Models' Reasoning Capabilities on LSAT Logic Games

• URL: http://arxiv.org/abs/2409.19012v1
• Date: Mon, 23 Sep 2024 21:37:40 GMT
• Title: Lost in the Logic: An Evaluation of Large Language Models' Reasoning Capabilities on LSAT Logic Games
• Authors: Saumya Malik,
• Abstract summary: I evaluate the performance of Large Language Models (LLMs) on the Law School Admissions Test (LSAT) I construct a dataset of logic games and their associated metadata, and extensively evaluate LLMs' performance in a Chain-of-Thought prompting setting. I analyze the types of logic games that models perform better or worse on, as well as the types of logical errors I observe from human annotation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this thesis, I evaluate the performance of Large Language Models (LLMs) on the Law School Admissions Test (LSAT), specifically the Logic Games section of the test. I focus on this section because it presents a complex logical reasoning task and thus is a valuable source of data for evaluating how modern, increasingly capable LLMs can handle hard logical reasoning tasks. I construct a dataset of LSAT logic games and their associated metadata, and extensively evaluate LLMs' performance in a Chain-of-Thought prompting setting. Given the weak performance in this setting, I explore other prompting frameworks on a smaller subset of the dataset, adapting ideas from Reflexion to this task. This results in a substantially improved accuracy of 70 percent for GPT-4 and 46 percent for GPT-3.5 on this data subset, highlighting the capacity of LLMs to revise their logical errors, despite initially weak performance. Finally, I analyze the types of logic games that models perform better or worse on, as well as the types of logical errors I observe from human annotation, providing detailed insights on the logical reasoning capabilities of LLMs.

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