Unverified50% confidenceFactExact time
The root causes of cross-model citation behavior differences include differences in pre-training corpora, differences in SFT datasets, and differences in the human annotator populations at the RLHF stage.
1
Sources
50%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
如何科学测试提示词对LLM引用行为的影响
redditr/learnmachinelearning7/9/2026
Related Claims
Unverified不同模型在抗诱导能力上的差异很大程度上源于后训练阶段(post-training)的策略差异77% similarUnverified模型之间的差异源于训练数据规模和质量、参数数量、对齐策略(如RLHF)以及上下文窗口长度等核心设计选择75% similarUnverified参数量相似的情况下,训练数据质量分布的差异往往比架构差异更能决定特定场景的表现上限74% similarUnverified不同模型系列的工具调用输出格式差异根源于指令微调阶段使用的训练数据差异73% similarUnverifiedModels trained with SFT (Supervised Fine-Tuning) have an 'imitation ceiling,' where their performance upper bound is constrained by the quality and diversity of the labeled data.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/607133API
curl https://kongchang.com/api/v1/knowledge/claims/607133MCP
get_claim(id=607133)