Summary of Flare: Faithful Logic-aided Reasoning and Exploration, by Erik Arakelyan et al.
FLARE: Faithful Logic-Aided Reasoning and Exploration
by Erik Arakelyan, Pasquale Minervini, Pat Verga, Patrick Lewis, Isabelle Augenstein
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces FLARE, a novel approach for traversing problem spaces using task decompositions. FLARE combines Large Language Models (LLMs) with logic programming codes to generate faithful intermediate chain of reasoning. Unlike other approaches that rely on external solvers or struggle with ambiguous tasks, FLARE uses LLMs to plan solutions and soft-formalise queries into facts and predicates. The method allows for faithfulness evaluation and analysis of the multi-hop search without relying on external solvers. FLARE achieves state-of-the-art results on 7 out of 9 diverse reasoning benchmarks, showing positive correlation between model faithfulness and overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand questions and answers by creating a new way to think through problems step-by-step. This approach uses big language models to plan solutions and turn questions into small, manageable pieces. The method is good at solving tricky problems that are hard to formalize. It even shows how the computer thinks through each step to get the right answer. The results are impressive, with the new method doing better than others on many types of reasoning tasks. |
