LLM-generated combinatorial solvers achieve highest correctness when the model formalizes problems for verified backends rather than attempting to optimize search, which often causes regressions.
Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=
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A neuro-symbolic system is proposed that uses formal logic to constrain LLM outputs so legal inferences stay faithful to source text.
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Formalize, Don't Optimize: The Heuristic Trap in LLM-Generated Combinatorial Solvers
LLM-generated combinatorial solvers achieve highest correctness when the model formalizes problems for verified backends rather than attempting to optimize search, which often causes regressions.
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Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
A neuro-symbolic system is proposed that uses formal logic to constrain LLM outputs so legal inferences stay faithful to source text.