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When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control | AI Learning Platform

BenchmarkSafety & AlignmentReasoning

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

Ali Şenol1 min readarXivSep 15, 2026

Key Takeaway for Students

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-cho

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Career Insight

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Why it matters

This paper introduces a practical, prompt-only method to improve LLM reliability by making models abstain when uncertain, directly addressing a critical safety concern. ML engineers can leverage such techniques to build more trustworthy AI systems, reducing hallucination and improving user confidence. Understanding and implementing selective risk control mechanisms is becoming crucial for deploying LLMs responsibly in production.

Skills to learn

Prompt engineering for self-correction and meta-reasoningDesigning and evaluating selective abstention mechanisms for LLMsImplementing and comparing different chain-of-thought prompting variantsQuantifying and mitigating hallucination rates in LLM outputsApplying TruthfulQA or similar benchmarks for model safety evaluation

Interview questions

Explain the concept of 'selective risk control' in LLMs and how Chain-of-Self-Questioning (CoSQ) addresses it. What are its advantages over traditional prompting?
Describe a scenario where an LLM's inability to abstain could lead to significant negative consequences. How would you design a system using CoSQ to mitigate this risk?
Design a system architecture for integrating a CoSQ-like abstention mechanism into a production LLM inference pipeline. Consider latency, cost, and maintainability. How would you handle cases where the model abstains?

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