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nlp:neurosymbolic_methods [2025/02/23 09:33] – [Overviews] jmflanignlp:neurosymbolic_methods [2025/08/30 00:35] (current) – [Papers] jmflanig
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   * [[https://arxiv.org/pdf/2211.11559|Gupta & Kembhavi 2022 - Visual Programming: Compositional visual reasoning without training]]   * [[https://arxiv.org/pdf/2211.11559|Gupta & Kembhavi 2022 - Visual Programming: Compositional visual reasoning without training]]
   * [[https://arxiv.org/pdf/2305.12744|Pan et al 2023 - Fact-Checking Complex Claims with Program-Guided Reasoning]]   * [[https://arxiv.org/pdf/2305.12744|Pan et al 2023 - Fact-Checking Complex Claims with Program-Guided Reasoning]]
 +  * [[https://arxiv.org/pdf/2305.12295|Pan et al 2023 - Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning]]
 +  * [[https://arxiv.org/pdf/2310.05253|Wang & Shu 2023 - Explainable Claim Verification via Knowledge-Grounded Reasoning with
 +Large Language Models]]
   * [[https://arxiv.org/pdf/2310.16035.pdf|Hsu et al 2023 - What’s Left? Concept Grounding with Logic-Enhanced Foundation Models]]   * [[https://arxiv.org/pdf/2310.16035.pdf|Hsu et al 2023 - What’s Left? Concept Grounding with Logic-Enhanced Foundation Models]]
 +  * **[[https://arxiv.org/pdf/2506.04592|Liu et al 2025 - Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification]]**
  
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nlp/neurosymbolic_methods.1740303201.txt.gz · Last modified: 2025/02/23 09:33 by jmflanig

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