Unverified60% confidenceFactTime unknown
LLMs exhibit Positional Bias, tending to focus more on the beginning and end of the input sequence, with information in the middle easily being forgotten.
2
Sources
60%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Claude Code Ultra Review实测：多Agent代码审查深度解析
bilibili量子菠萝_
Related Entities
Related Claims
VerifiedLLMs exhibit a 'Lost in the Middle' phenomenon, where the model pays more attention to information at the beginning and end of the context while tending to overlook information in the middle.75% similarUnverifiedLLM自动评估检索结果时可能存在偏差，如偏好长度更长、语言更流畅的段落，或对特定领域知识有盲区，因此小规模人工标注金标准仍不可替代。74% similarUnverifiedPlacing key information at the beginning and end of the input can mitigate the impact of the 'lost in the middle' effect on results.70% similarUnverifiedCurrent LLM Agents typically rely on implicit inference from the match record within the context window and lack an explicit belief-state maintenance mechanism, making them prone to forgetting early clues or overfitting to recent turns in long games.70% similarUnverifiedThe recall and precision of the retrieval stage often have a greater impact on final answer quality than the choice of LLM itself.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/16254API
curl https://kongchang.com/api/v1/knowledge/claims/16254MCP
get_claim(id=16254)