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

Paper recorded by Signals 4 on 2026-09-15 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16

Category: cs.AI · 人工智能 · first seen 2026-09-16

Abstract

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. We evaluate three CoSQ variants under seventeen conditions on the 817-item TruthfulQA multiple-choice validation set u

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#44 most recent of 300 cs.AI papers we have recorded · ↑ newer: PhysStream: Streaming Physics-Grounded Video Generation with Structure · ↓ older: LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs
Cite this page: When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control: the #44 most recent of 300 cs.AI papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/when-should-llms-abstain-chain-of-self-questioning-for-selective-risk-control.html
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