ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning

• URL: http://arxiv.org/abs/2502.01100v1
• Date: Mon, 03 Feb 2025 06:44:49 GMT
• Title: ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
• Authors: Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, Yejin Choi,
• Abstract summary: We introduce ZebraLogic, a comprehensive evaluation framework for assessing LLM reasoning performance. ZebraLogic enables the generation of puzzles with controllable and quantifiable complexity. Our results reveal a significant decline in accuracy as problem complexity grows.
• Score: 92.76959707441954
• License:
• Abstract: We investigate the logical reasoning capabilities of large language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a comprehensive evaluation framework for assessing LLM reasoning performance on logic grid puzzles derived from constraint satisfaction problems (CSPs). ZebraLogic enables the generation of puzzles with controllable and quantifiable complexity, facilitating a systematic study of the scaling limits of models such as Llama, o1 models, and DeepSeek-R1. By encompassing a broad range of search space complexities and diverse logical constraints, ZebraLogic provides a structured environment to evaluate reasoning under increasing difficulty. Our results reveal a significant decline in accuracy as problem complexity grows -- a phenomenon we term the curse of complexity. This limitation persists even with larger models and increased inference-time computation, suggesting inherent constraints in current LLM reasoning capabilities. Additionally, we explore strategies to enhance logical reasoning, including Best-of-N sampling, backtracking mechanisms, and self-verification prompts. Our findings offer critical insights into the scalability of LLM reasoning, highlight fundamental limitations, and outline potential directions for improvement.

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