Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?

ACL ARR 2024 June Submission4949 Authors

16 Jun 2024 (modified: 30 Jul 2024)ACL ARR 2024 June SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Abstract: Solving grid puzzles involves a significant amount of logical reasoning. Hence, it is a good domain to evaluate reasoning capability of a model which can then guide us to improve the reasoning ability of models. However, most existing works evaluate only the final predicted answer of a puzzle, without delving into an in-depth analysis of the LLMs' reasoning chains (such as where they falter) or providing any finer metrics to evaluate them. Since LLMs may rely on simple heuristics or artifacts to predict the final answer, it is crucial to evaluate the generated reasoning chain beyond overall correctness measures, for accurately evaluating the reasoning abilities of LLMs. To this end, we first develop GridPuzzle, an evaluation dataset comprising of 274 grid-based puzzles with different complexities. Second, we propose a new error taxonomy derived from manual analysis of reasoning chains from LLMs including GPT-4, Claude-3, Gemini, Mistral, and Llama-2. Then, we develop a LLM-based framework for large-scale subjective evaluation (i.e., identifying errors) and an objective metric, PuzzleEval, to evaluate the correctness of reasoning chains. Evaluating reasoning chains from LLMs leads to several interesting findings. We further show that existing prompting methods used for enhancing models' reasoning abilities do not improve performance on GridPuzzle. This highlights the importance of understanding fine-grained errors and presents a challenge for future research to enhance LLMs' puzzle-solving abilities by developing methods that address these errors.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: Puzzle-solving, Logical Reasoning, Automated Evaluation
Contribution Types: Model analysis & interpretability, Data resources
Languages Studied: English
Submission Number: 4949
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