Unverified75% confidenceFactExact time
添加行号后模型能更准确地定位代码位置，减少行号幻觉
1
Sources
75%
Confidence
Long-term
Relevance
4/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedContent with clear structure and unambiguous semantics is easier for LLMs to parse and can effectively reduce the risk of the model generating hallucinations.77% similarUnverified领域特化的 Embedding 模型（如代码领域的 CodeBERT）往往比通用模型在垂直场景下具有更高的语义检索精度72% similarUnverified将逻辑固化为可复用代码比每次让大模型重新推理更快且节省Token，具有确定性和可审计性72% similarVerifiedFill-in-the-Middle (FIM) training provides the model with a code prefix and suffix and asks it to predict the missing middle portion, which more closely resembles real-world code completion scenarios.70% similarUnverifiedThe inclusion of code data has been shown to significantly enhance a model's logical reasoning ability even on non-code tasks, a phenomenon known as the spillover effect of code data.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/35617API
curl https://kongchang.com/api/v1/knowledge/claims/35617MCP
get_claim(id=35617)