Papers with LINC
LTRAG: Enhancing Autoformalization and Self-refinement for Logical Reasoning with Thought-Guided RAG (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown promise in natural language reasoning, especially with techniques like chain-of-thought prompting. |
| Approach: | They propose a framework to enhance autoformalization and self-refinement for logical reasoning with Retrieval-Augmented Generation (RAG) by building knowledge bases of thought-guided examples. |
| Outcome: | The proposed framework outperforms Logic-LM and LINC on FOLIO and AR-LSAT, and achieves an accuracy gain of 13% over Logic LM and the proposed methods on GPT-4 and AR LSAT. |
LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers (2023.emnlp-main)
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Theo Olausson, Alex Gu, Ben Lipkin, Cedegao Zhang, Armando Solar-Lezama, Joshua Tenenbaum, Roger Levy
| Challenge: | Logical reasoning is an important task for artificial intelligence, says a new study . many prompting-based strategies to enable large language models fail in subtle and unpredictable ways. |
| Approach: | They propose to reformulate logical reasoning tasks by leveraging large language models . they use a modular neurosymbolic programming approach to translate premises and conclusions from natural language to logic . |
| Outcome: | The proposed approach outperforms open-source models on FOLIO and ProofWriter while showing distinct failure modes. |