Chain-of-Verification reduces hallucinations in large language models by drafting responses, planning independent verification questions, answering them separately, and generating a final verified output.
Transactions of the Association for Computational Linguistics , volume=
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Pragmatic reasoning in LLMs varies substantially by evaluation method and model family, with scalar diversity patterns appearing only in certain conditions rather than reflecting stable competence.
A reference-free proxy scoring framework combined with GIRB calibration produces better-aligned evaluation metrics for summarization and outperforms baselines across seven datasets.
citing papers explorer
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Chain-of-Verification Reduces Hallucination in Large Language Models
Chain-of-Verification reduces hallucinations in large language models by drafting responses, planning independent verification questions, answering them separately, and generating a final verified output.
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Evaluating Pragmatic Reasoning in Large Language Models: Evidence from Scalar Diversity
Pragmatic reasoning in LLMs varies substantially by evaluation method and model family, with scalar diversity patterns appearing only in certain conditions rather than reflecting stable competence.
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Calibrating Model-Based Evaluation Metrics for Summarization
A reference-free proxy scoring framework combined with GIRB calibration produces better-aligned evaluation metrics for summarization and outperforms baselines across seven datasets.