Unverified50% confidenceFactExact time
The autoregressive decoding nature of LLMs inherently favors high-probability sequences, and alignment training such as RLHF further reinforces dominant patterns of human preference, causing generated dialogue strategies to be highly concentrated.
1
Sources
50%
Confidence
Long-term
Relevance
10/1/2026
First Seen
Sources
Related Entities
Related Claims
Verified大语言模型在RLHF训练阶段，因人类标注员倾向于给更详细、更礼貌的回答打高分，导致模型学会了过度冗长的表达模式80% similarVerifiedModern LLMs have acquired capabilities for planning, reasoning, and tool use through instruction tuning and reinforcement learning from human feedback (RLHF).80% similarUnverifiedMost mainstream large models today are trained on similar internet corpora and adopt similar architectures and alignment methods (such as RLHF), potentially inheriting the same biases and tendencies77% similarVerifiedRLHF（基于人类反馈的强化学习）是ChatGPT等对话模型背后的关键训练技术77% similarUnverifiedRLHF的人类介入发生在训练阶段并固化为模型权重，属于隐式编码，而表征工程干预在推理时操控内部表征，具有即时性和可逆性76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/963990API
curl https://kongchang.com/api/v1/knowledge/claims/963990MCP
get_claim(id=963990)