LLMs struggle as formalizers for constraint satisfaction problems, research finds

A new research paper explores the effectiveness of large language models (LLMs) when used as formalizers for constraint satisfaction problems. The study, which investigated LLMs on real-life problems across various benchmarks and formal languages, found that while this approach offers verifiability and interpretability, it often underperforms when LLMs are used as end-to-end solvers. The research also observed that LLM-as-formalizer performance degrades significantly with increasing problem complexity, sometimes leading to hard-coded solutions. AI

IMPACT Highlights limitations in LLM reasoning for complex symbolic tasks, suggesting further research is needed for reliable formalization.

RANK_REASON Academic paper on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Li Zhang

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