Unverified50% confidenceFactExact time
LLMs are highly sensitive to certainty signals in language expression, and high-confidence erroneous assertions can mislead other agents into abandoning correct judgments.
1
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50%
Confidence
Long-term
Relevance
10/1/2026
First Seen
Sources
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Related Claims
UnverifiedLLM 在处理语法错误、拗口句式、被动语态滥用、冗余表达等表层语言问题上表现出色，能在几秒内给出流畅的改写建议75% similarUnverifiedThe ambiguity of natural language instructions in prompts, compounded by the uncertainty of LLM interpretation, makes prompt configuration drift harder to detect with diff tools than traditional configuration management.72% similarVerifiedLarge language models have a miscalibration problem, often answering in a confident tone regardless of whether the answer is correct or incorrect.71% similarUnverifiedWriting scoring rules into a prompt in natural language has drawbacks: wording variations across different calls may cause the model's understanding to drift, and the scoring logic cannot be directly parsed or reused by programs.71% similarUnverifiedDeterministic guardrails can only handle fixed-pattern content that looks like sensitive information and cannot detect natural language risks where sentences are fluent but subtly guide users toward incorrect actions.71% similar
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