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New methods recover evidence passages for LLM verdicts without human annotation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new methods to recover evidence passages for review workflows when only the final verdict is available. Label-only post-training and rejection sampling techniques were tested on the ContractNLI dataset. Both methods showed improvements in accuracy and span F1 scores compared to pre-training, with label-only training achieving an accuracy of 0.896 and span F1 of 0.564. These approaches aim to enhance evidence recovery without requiring human annotation of the passages. AI

IMPACT Improves the ability to reconstruct supporting evidence for LLM-generated verdicts without manual annotation.

RANK_REASON Research paper detailing new methods for evidence recovery in LLM workflows. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New methods recover evidence passages for LLM verdicts without human annotation

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Research paper detailing new methods for evidence recovery in LLM workflows. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nishanth Nayakanti, Prasang Gupta, Ashutosh Bilthare, Kevin Paul ·

    Verdicts Without Annotated Evidence: Rejection Sampling or Label-Only Post-Training for Evidence Recovery?

    arXiv:2610.06962v1 Announce Type: cross Abstract: In many review workflows the verdict is the only thing retained. The passages behind it are not marked, because that annotation costs far more than recording the decision. We measure how much of that evidence a small language mode…

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