Expired50% confidenceFactExact time
Large language models exhibit a "sycophancy" tendency, inclining toward affirmative answers when asked to judge whether a task is complete.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
Related Claims
UnverifiedSycophancy bias refers to the tendency of a model to cater to users' views even when the user's judgment is wrong, originating from annotators' implicit preference for pleasing answers in RLHF training.71% similarUnverifiedReasoning tasks require a strong logical chain while summarization tasks demand concise language ability, and using the same model for both not only wastes computing power but may also not yield the best results.70% similarUnverified使用包含引导语的Chain-of-Thought提示，可使大语言模型在复杂推理任务上准确率提升40%以上70% similarVerifiedThe emergent capability of large language models refers to the complex reasoning ability that suddenly manifests after the parameter scale surpasses a certain threshold.70% similarUnverifiedLarge language models can approximately estimate decision confidence by analyzing token probability distributions, explicitly outputting self-evaluation scores, or observing answer consistency across multiple samplings.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/42546API
curl https://kongchang.com/api/v1/knowledge/claims/42546MCP
get_claim(id=42546)