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拒答与校验:无答案比错答案好
RAG 系统最伤信任的时刻,不是「答不出来」,而是「一本正经地答错」。拒答机制的目标是让系统在该沉默时沉默:检索层无料就明说,生成层答案没依据就降级,输出前再加一道 groundedness 校验。本文讲清三道防线的实现、阈值设计与「拒答不冷漠」的体验设计。
大模型 精选 · 原创 · 昨天 阅读 0·访客 0
引用与溯源:让每个答案都有出处
RAG 区别于裸聊天的核心承诺是「答案有据可查」——但很多系统的引用是装饰品:编号对不上、引用了上下文里没有的来源、或干脆一句「根据资料」。本文讲清引用体系的完整链路:chunk 稳定标识、生成时的引用协议、后校验防编造、句子级 attribution 与前端呈现。
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Microsoft AI Releases Microsoft-Decision-1: A Qwen3.5-9B Decision-Scoring Model
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大模型 MarkTechPost · 10-10 阅读 243·访客 243
A Developer’s Guide to Laya: Zero-Shot Decisions and Calibration
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开源项目 MarkTechPost · 10-7 阅读 53·访客 53
Competence-Gated Pooling of Language Models and Priors for Event Forecasting
In hybrid forecasting, a language model is often one of several available signals. A system may already have a market, c…
行业动态 HuggingFace Daily Papers · 9-10 阅读 25·访客 25
Recognition-Refusal Misalignment in LLMs: Why Models Answer Structurally Unanswerable Questions
Large language models often answer structurally unanswerable questions, such as computing cot(-540°) or evaluating (1).s…
大模型 HuggingFace Daily Papers · 8-29 阅读 17·访客 16