Unverified50% confidenceFactExact time
Existing LLMs optimize for "human preference," whereas new models like Jev optimize for "calibrated decisions"—that is, cognitively honest probabilistic answers.
1
Sources
50%
Confidence
Long-term
Relevance
9/23/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedDuring training, LLMs optimize parameters by maximizing the prediction probability of the next token, meaning the model learns the statistical distribution of the training corpus rather than human aesthetic judgment.72% similarUnverifiedTraditional LLMs, after RLHF human preference alignment, tend to give confidently worded answers, which distorts probability estimates.72% similarUnverifiedMultiple studies in 2024 showed that LLMs exhibit a significant "self-consistency bias," tending to give overly favorable evaluations when asked to assess content they themselves generated.70% similarUnverifiedDifferent LLM models vary in their accuracy of understanding instructions; it is recommended to iteratively optimize prompts multiple times to achieve the best results.70% similarUnverifiedLLM本质上是在做条件概率预测，擅长回答字面问题而非用户真正需求，这种现象被称为字面遵从（Literal Compliance）70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/943257API
curl https://kongchang.com/api/v1/knowledge/claims/943257MCP
get_claim(id=943257)