Interview Bank

RAG in 14 Days: From Retrieval to Trustworthy Answers

D8 Evaluation First: Building a Golden Set, Computing Recall and Ranking Metrics, Using a Model as Judge for Faithfulness

• What systematic biases does an LLM judge have when scoring RAG faithfulness, and how do you detect them and prove your judge is trustworthy?用模型当裁判来评 RAG 的忠实度，有哪些系统性偏差？你怎么发现它们、又怎么证明你的裁判可信？

  Common in ChinaCommon overseasDeep dive#llm-as-judge#evaluation#faithfulness

  How to reason about it · think before answering

  1. The second half of the question is the discriminator. Plenty of people can name position, length, and self-preference bias; few can say how they prove the judge is trustworthy.
  2. Pair each bias with its mitigation: position bias - score pointwise instead of pairwise, and if you must compare, swap the order and call disagreement a tie; length bias - decompose into claims and score a ratio, so a longer answer grows its own denominator; self-preference - judge with a different vendor or tier than the generator.
  3. Add two prompt-level requirements: fixed rubric anchors (spell out what 1.0, 0.6 and 0.3 mean, or the same input scores differently on different days) and forced structured output that quotes the unsupported sentences verbatim, which is what makes human review possible.
  4. Proving trust has exactly one route: human spot-checks and an agreement rate. Stratify ten to thirty items across types, hits and misses, high and low judge scores; answer one binary question only - is anything here not in the material - and compare. Below 0.8 the judge's scores cannot gate a merge.
  5. A detail that scores points: a very high agreement rate may mean your spot-check was too easy. If all ten sampled answers copy the material verbatim, agreeing is trivial and 100% says nothing about the judge.
  6. Expected follow-up: can the judge itself break? Add probes - fixed inputs with known verdicts, one faithful and one obviously fabricated, checked on every run. An evaluation system fails silently: the numbers keep coming, they just stop meaning anything.

  分析过程 · 先想清楚再作答

  1. 这题的题眼在后半句。能背出「位置偏好、长度偏好、自我偏好」三个名词的人很多，能说出「怎么证明可信」的很少——面试官要的是后者。
  2. 先把三个偏差和各自的缓解手段一一对应：位置偏好用逐条独立打分代替两两比较，非要比较就交换顺序跑两遍、结论不一致判平局；长度偏好用逐句判定加比例计分，写得越长分母越大，长度红利自动消失；自我偏好用跨供应商或跨档位的模型评判，生成和评判不同源。
  3. 再补两条提示词层面的：给死评分锚点，1.0 / 0.6 / 0.3 各自是什么必须写明，否则同一份输入不同天给的分都不一样；强制结构化输出并要求把没支撑的句子原样列出，这是人工复核的抓手。
  4. 证明可信只有一条路：人工抽检算一致率。分层抽十到三十条——各类型都要有、命中和没命中都要有、裁判给高分和低分都要有，只判一个二元问题（有没有材料外的内容），跟裁判的结论比对。低于 0.8 就不能拿它的分数做拦合并这类决策。
  5. 一个能加分的细节：一致率很高不一定是好消息。如果抽的十条都是「答案原样抄自材料」的简单题，判对是理所当然的，这时候 100% 说明的是抽检没难度，不是裁判可靠。
  6. 可预期的追问是「裁判本身会不会坏」。答案是给裁判写探针：喂几组已知正确答案的输入（照抄材料的、明显编造的），每次跑评估都验一遍——评估系统坏掉的方式最阴险，分数照常输出，只是不再有意义。

  Key points

  • Three biases: position, verbosity, and self-preference, each with a matching mitigation.
  • Score pointwise rather than pairwise; decompose into claims and score a ratio to kill the length premium; never let the generator judge itself.
  • Pin rubric anchors in the prompt and force structured output that quotes unsupported sentences.
  • Establish trust through stratified human spot-checks and an agreement rate; below 0.8 the judge cannot gate merges.
  • Add probes with known verdicts so a broken judge is caught on every run.

  答题要点

  • 三个偏差：位置偏好、偏爱长答案、自己评自己，各自有对应的缓解手段。
  • 逐条独立打分代替两两比较；逐句判定按比例计分抵消长度红利；生成与评判不同源。
  • 提示词要给死评分锚点，并强制结构化输出、列出没支撑的句子。
  • 可信度靠人工分层抽检算一致率，低于 0.8 不能用它做拦合并的决策。
  • 给裁判本身写探针，每次跑评估都验一遍它有没有坏。