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When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control

arXiv cs.AI / cs.LG / cs.CLby Ali Şenol ()1 min read1
AI summary · glm-5.3Summaries by Vani AI

Chain-of-Self-Questioning prompting cuts LLM wrong-answer commitments 32% relative while raising answered accuracy, holding across eleven model families.

The paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes LLM answer commitment conditional on an explicit assessment of the information required to answer. On an 817-item TruthfulQA multiple-choice set, Grounded-CoSQ at τ=0.90 reduced mean unconditional wrong-commitment rate from 13.1% under chain-of-thought to 8.9% (a 32.1% relative reduction), while raising answered accuracy from 86.9% to 89.7% at 87.6% coverage. Improvements held across eleven open-weight and hosted model families and at every evaluated threshold, with convergent evidence from a Natural Questions short-answer evaluation.

Key facts
  • Grounded-CoSQ at τ=0.90 cuts wrong commitments 32.1% relative vs chain-of-thought
  • Answered accuracy rose from 86.9% to 89.7% at 87.6% coverage
  • Improvements hold across eleven model families and all evaluated thresholds
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From the original article

1 min read at arxiv.org

Large language models can produce fluent answers when their factual support is weak. This paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes answer commitment conditional on an explicit assessment of the information required to answer a question.

arXiv cs.AI / cs.LG / cs.CL

By Ali Şenol · arxiv.org

Read the full article at arXiv cs.AI / cs.LG / cs.CL

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.