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Large language models (LLMs) are increasingly explored as general-purpose reasoners, particularly in agentic contexts.
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G. D. Giacomo, Y. Lespérance, and A. R. Pearce · 2010
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M. Thielscher · 2010
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Reasoning about general games described in gdl-ii
S. Schiffel and M. Thielscher · 2011
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The general game playing description language is universal
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Swi-prolog
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Draft, sketch, and prove: Guiding formal theorem provers with informal proofs
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Chain-of-thought prompting elicits reasoning in large language models
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Autoformalization with large language models
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Coupling large language models with logic programming for robust and general reasoning from text
Z. Yang, A. Ishay, and J. Lee · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
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Foundational challenges in assuring alignment and safety of large language models
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Nl2tl: Transforming natural languages to temporal logics using large language models
Y. Chen, R. Gandhi, Y. Zhang, and C. Fan · 2023
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nl2spec: interactively translating unstructured natural language to temporal logics with large language models
M. Cosler, C. Hahn, D. Mendoza, F. Schmitt, and C. Trippel · 2023
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Language models can be logical solvers
J. Feng, R. Xu, J. Hao, H. Sharma, Y. Shen, D. Zhao, and W. Chen · 2023
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GPT agents in game theory experiments
F. Guo · 2023
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Solving math word problems by combining language models with symbolic solvers
J. He-Yueya, G. Poesia, R. E. Wang, and N. D. Goodman · 2023
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Mathprompter: Mathematical reasoning using large language models
S. Imani, L. Du, and H. Shrivastava · 2023
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Gtbench: Uncovering the strategic reasoning limitations of llms via game-theoretic evaluations
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Llms can’t plan, but can help planning in llm-modulo frameworks
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