Unverified50% confidenceFactExact time
对比学习通过构造正样本对和负样本对训练模型，SimCSE通过对同一句子应用不同Dropout掩码生成正样本对，无需人工标注
1
Sources
50%
Confidence
Long-term
Relevance
7/6/2026
First Seen
Sources
Building a RAG Q&A Workflow with Dify: A 4-Step Guide to Enterprise Knowledge Base Chatbots
bilibiliAI大模型大课堂6/30/2026
Related Claims
UnverifiedAlignment training optimizes the model's statistical behavior in known scenarios rather than establishing formally verifiable security boundaries, and may fail when faced with rare instruction combinations or role-playing frameworks outside the training distribution.72% similarUnverified训练数据在输入模型前会经过严格的去重、过滤和质量筛选，低质量内容、重复文本、有害内容会被剔除71% similarUnverifiedDuring SFT training, the model only computes loss on the assistant portion (loss masking technique), meaning it only learns how to answer, not how to ask questions.70% similarUnverified在没有额外约束的情况下，模型倾向于生成训练数据中出现频率最高的编码模式，而非特定团队的内部约定69% similarUnverifiedSimply stacking parameters without sufficient training data results in a model that is large but not capable; a leap in parameters must be accompanied by a proportionally expanded training corpus and computing power to realize capability gains.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/115518API
curl https://kongchang.com/api/v1/knowledge/claims/115518MCP
get_claim(id=115518)