Interview Bank

RAG in 14 Days: From Retrieval to Trustworthy Answers

D3 Getting Documents In: Parsing PDF and HTML, Tables and Scans, Cleaning Rules, and Metadata You Must Keep

• OCR output from scanned documents carries a non-trivial error rate. How does that noise propagate into retrieval and generation, and how do you mitigate it?扫描件走光学字符识别之后错字率不低，这些噪声会怎样影响检索和生成？怎么缓解？

  Common in ChinaCommon overseasDeep dive#ocr#data-quality#hybrid-search

  How to reason about it · think before answering

  1. This tests whether you can trace propagation rather than recite that OCR makes mistakes. The differentiator is separating how retrieval fails from how generation fails, because the two failure modes are entirely different.
  2. Retrieval first. Chinese OCR errors are mostly visually similar characters. Keyword search is literal, so one wrong character makes the term unmatchable, and bigram tokenisation makes it worse because a single wrong character corrupts two adjacent tokens. Recall drops quietly and nothing raises an error.
  3. Generation second. The model usually reads through minor noise, but when the corrupted token is a key entity such as a name, a model number, an amount or a date, it answers confidently with the wrong value. Citation checking degrades too: verifying against a source that is itself wrong proves nothing.
  4. Mitigate in three layers. At ingest, use an empty-text assertion to decide whether the PDF even needs OCR, and keep a link to the original image so a human can verify.
  5. At retrieval, hybrid search absorbs some of the damage because dense retrieval is less sensitive to a single wrong character than literal matching. At generation, mark low-confidence pages so the answer can state that the source came from a scan and may contain recognition errors.
  6. Expected follow-up: can you auto-correct? Yes, but carefully. Dictionary or model based post-processing fixes some errors and breaks correct proper nouns. Restrict correction to low-confidence spans and keep the raw text so you can fall back.

  分析过程 · 先想清楚再作答

  1. 这题考的是你会不会顺着链条推传导，而不是背「OCR 会有错字」这句废话。判据是有没有分别说清「检索侧怎么错」和「生成侧怎么错」——它们的失效方式完全不同。
  2. 先说检索侧。中文 OCR 的错主要是形近字，「已」认成「己」、「板」认成「版」。关键词检索是字面匹配，一个字错了这个词就查不到；更隐蔽的是二元组分词会连带毁掉相邻两个词元，一个错字影响的其实是两处。这一路的表现是召回悄悄掉下去，而且不报错。
  3. 再说生成侧。错字进了上下文，模型往往能读懂大意，但一旦是关键实体（人名、型号、金额、日期）出错，它会照着错的答，而且答得很自信。更麻烦的是引用校验也会跟着失效——原文本身就是错的，校验通过了也没意义。
  4. 缓解按三层说。入口层：先用空文本比例这类断言判断这份 PDF 有没有文本层，有就别走 OCR；真要走，保留原图链接以便人工复核。
  5. 检索层：靠混合检索兜底，向量一路对个别错字不敏感，能补上关键词一路的失手，这是 D9 那套东西在这里的具体价值。生成层：把低置信度的页面标出来，让模型在引用它们时明确提示「该材料来自扫描件，可能有识别误差」。
  6. 可预期的追问：能不能自动纠错？可以但要克制——用词典或模型做后处理会修好一批，也会「修」坏一批原本正确的专有名词。稳妥的做法是只对置信度低的片段做纠错，并且保留原文以便回退。

  Key points

  • Retrieval: visually similar characters break literal matching, and bigram tokenisation lets one bad character corrupt two tokens, so recall drops silently.
  • Generation: the model reads through general noise but confidently repeats corrupted entities, and citation verification against a corrupted source proves nothing.
  • At ingest: check for a text layer before running OCR at all, and keep the source image for human verification.
  • At retrieval: hybrid search helps because dense retrieval tolerates a single wrong character better than literal matching.
  • At generation: flag low-confidence sources in the answer, and restrict auto-correction to low-confidence spans while keeping the raw text.

  答题要点

  • 检索侧：形近字让字面匹配直接查不到，二元组分词还会让一个错字毁掉相邻两个词元，表现是召回悄悄下降且不报错。
  • 生成侧：模型能读懂大意，但关键实体出错时会自信地答错，引用校验也失去意义。
  • 入口层缓解：先判断有没有文本层再决定要不要 OCR，并保留原图链接供人工复核。
  • 检索层缓解：混合检索里的向量一路对个别错字不敏感，能兜住关键词一路的失手。
  • 生成层缓解：标出低置信度来源，让回答显式提示可能存在识别误差；自动纠错只对低置信片段做并保留原文。