The proposed Faithful-Refiner, combining syntactic parsing, quantifier and consistency checks, logical-relation guidance, and detailed proof feedback, raises explanation refinement rates on three NLI benchmarks by large margins over the prior Explanation-Refiner baseline.
A Survey on Explainability in Machine Reading Comprehension
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abstract
This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference challenges evolved and the steps which were taken to tackle these challenges. We also present the evaluation methodologies to assess the performance of explainable systems. In addition, we identify persisting open research questions and highlight critical directions for future work.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations
The proposed Faithful-Refiner, combining syntactic parsing, quantifier and consistency checks, logical-relation guidance, and detailed proof feedback, raises explanation refinement rates on three NLI benchmarks by large margins over the prior Explanation-Refiner baseline.