Research·arXiv cs.CL·13h ago

Prompt technique cuts LLM false confidence by a third across eleven models

Researchers have developed Chain-of-Self-Questioning, a prompt-based technique that conditions LLM responses on explicit confidence assessment, enabling models to decline answering when factual grounding is weak. Testing across eleven model families on TruthfulQA shows Grounded-CoSQ reduces incorrect confident answers by 32% while maintaining 87.6% answer coverage and improving accuracy to 89.7%. This addresses a core reliability problem in production LLMs: fluent but unfounded responses. The prompt-only approach requires no retraining, making it immediately applicable across deployed systems seeking to balance utility against hallucination risk.

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Explainer

The key constraint here is that this works without retraining or fine-tuning. Most confidence-calibration work requires model modification; Chain-of-Self-Questioning achieves comparable risk reduction through prompting alone, which means it can be deployed as a wrapper on any existing model in production today.

This complements the self-improvement angle from ScienceBuddy (September). Where ScienceBuddy focuses on agents that learn and refine their execution loops over time, Chain-of-Self-Questioning addresses a more immediate problem: making individual model calls more reliable right now. Both papers assume deployed systems need to operate in real conditions rather than remain static, but they work at different layers. ScienceBuddy optimizes the agent's decision-making framework; this paper optimizes the model's willingness to say 'I don't know.' Together they sketch a picture of production AI that's both adaptive and cautious.

If the 32% reduction in confident errors holds when tested on out-of-distribution factual benchmarks (not just TruthfulQA variants), that signals real robustness. If major LLM providers integrate this into their default system prompts within six months, adoption will confirm it's practical enough to become standard. If accuracy drops below 85% on any major benchmark when abstention is enabled, the trade-off becomes harder to justify.

Coverage we drew on

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents · arXiv cs.CL

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MentionsTruthfulQA · Chain-of-Self-Questioning · Grounded-CoSQ

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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.