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One testing strategy for uncertainty in AI system outputs is LLM-as-Judge: using another model to score output quality.
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10/12/2026
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UnverifiedAdding schema validation at the tool-calling stage constrains the uncertainty of model outputs to a controllable range.76% similarUnverifiedSome teams have introduced an LLM-as-Judge approach, using another model to evaluate agent output quality in order to form an automated quality feedback loop74% similarUnverifiedThere are two approaches to implementing a large model's ability to decline to answer: instructing it to express uncertainty in the system prompt, or adding a confidence-scoring node in the Agent pipeline to route questions to a human agent.72% similarUnverifiedResearch directions for improving model calibration include introducing uncertainty tokens through supervised fine-tuning, performing post-calibration on probability distributions using temperature sampling, and training models to proactively output the ability to refuse to answer.72% similarUnverified2023年多项AI研究表明，让多个LLM实例互相质疑和反驳能有效减少单一模型的幻觉问题，提升推理准确率71% similar
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