BAG prompts LLMs to reason over K sampled responses for strategy selection in multi-turn ambiguous QA, improving accuracy and faithfulness to uncertainty over baselines across six models.
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In a Gaussian single-index model, neural reward models recover the hidden direction for β1 above an O(1) threshold and provide tilted-policy value-gap bounds for label-weighted and surrogate-weighted exponential fits.
ConSUM reranks candidate summaries using MBR consensus and source-consistency metrics to improve factuality over standard generation or reranking baselines.
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Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation
BAG prompts LLMs to reason over K sampled responses for strategy selection in multi-turn ambiguous QA, improving accuracy and faithfulness to uncertainty over baselines across six models.
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Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding
ConSUM reranks candidate summaries using MBR consensus and source-consistency metrics to improve factuality over standard generation or reranking baselines.