面试题库

14 天 RAG：从检索到可信回答

D10 查询侧优化：改写、假设文档嵌入、多路查询、后退提问与意图路由

• 假设文档嵌入（HyDE）为什么有效？它在什么情况下会把检索带偏？Why does HyDE (hypothetical document embeddings) work, and when does it steer retrieval in the wrong direction?

  国内高频海外高频进阶#hyde#query-transformation#retrieval-quality

  分析过程 · 先想清楚再作答

  1. 这题的题眼在后半句。前半句网上到处都能抄到，能不能说清「什么时候不该用」才是区分度所在——只答前半句的人，多半没在真实语料上跑过。
  2. 先给机制：向量检索比的是语义相似度，而用户的疑问句和文档里的制度条文在文体、句式、用词上都不同类。HyDE 先让模型编一段「长得像目标文档」的假文本，用它的向量去找邻居，等于把查询搬进了文档所在的那个语域。
  3. 紧接着点破一个常见误解：这段假文本的**事实对不对根本不重要**，因为它不给用户看，只贡献一个向量方向。理解到这一层，才算真懂它为什么不怕模型瞎编。
  4. 带偏有两种典型情况。一是模型编得太具体，给出语料里根本不存在的字段名或流程名，向量朝着一个不存在的方向去了；二是语料里压根没有答案，本该拒答的问题被编出来的假文档匹配到几个「看起来挺像」的邻居，拒答率掉下去、瞎编率涨上来。
  5. 说完风险要给对策，这一步最见工程经验：门槛卡在**每一路检索器的原始分**上而不是融合分上（融合分是相对的，最不相干的一批也能拿最高分）；以及把假设文档当成**第二个检索式与原问题融合**，而不是直接替换原问题——替换在模型编歪时会把原问题的信号一起丢掉。
  6. 可预期的追问是「它多花多少钱」。答：假设文档要写上百字，输出 token 是查询改写的十几倍，是查询侧四种手法里最贵的一次调用，而且检索次数翻倍。所以它通常不该默认打开，应该进 A/B 队列。

  How to reason about it · think before answering

  1. The tell is in the second half. Anyone can recite why HyDE works; only someone who has run it on real data can say when it hurts.
  2. Give the mechanism first: dense retrieval compares semantic similarity, but a user's question and a policy paragraph differ in register, syntax and vocabulary. HyDE has the model draft a fake passage that looks like the target document, then retrieves with that vector — effectively moving the query into the documents' register.
  3. Then kill the common misreading: the factual accuracy of the draft does not matter, because it is never shown to the user. It only contributes a direction in embedding space.
  4. Two failure modes. The model invents an over-specific field or process name that does not exist in the corpus, and the vector chases something imaginary. Or the corpus genuinely has no answer, and the fabricated passage finds plausible-looking neighbours anyway — abstention rate drops and hallucination rate climbs.
  5. Pair the risk with a mitigation: gate admission on each retriever's raw score, never on the fused score (fused scores are relative, so even the worst batch tops out at 1.0); and treat the hypothetical document as a second query fused with the original rather than a replacement, so a bad draft can only dilute the signal, not erase it.
  6. Expect the follow-up on cost. The draft runs to a hundred-plus output tokens, an order of magnitude more than a rewrite, and it doubles retrieval calls. That is why it belongs in an A/B queue, not in the default config.

  答题要点

  • 有效的原因是语域对齐：疑问句和制度条文本来不在一个语义邻域，假设文档把查询搬到了文档那一侧。
  • 假文本的事实对错不重要，它只贡献一个向量方向，不展示给用户。
  • 带偏的两种情况：编得太具体，追一个语料里不存在的方向；本该拒答的问题被假文档匹配上，拒答率下降。
  • 两条护栏：门槛卡原始分不卡融合分；把假设文档当第二个检索式融合，而不是替换原问题。
  • 成本上它是查询侧最贵的一项（长输出加检索次数翻倍），默认关闭、按场景 A/B。

  Key points

  • It works by register alignment: a question and a policy paragraph sit in different neighbourhoods, and the fake passage moves the query into the document's.
  • The draft's factual accuracy is irrelevant — it only supplies a direction and is never shown to the user.
  • It misfires when the model invents over-specific details, or when the corpus has no answer and the fabrication finds plausible neighbours anyway.
  • Two guardrails: gate on raw per-route scores, not fused ones; fuse the hypothetical document with the original query instead of replacing it.
  • It is the most expensive query-side technique (long output plus doubled retrievals), so keep it off by default and A/B it.