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- SKILL.md: 3 new entries (exploration-over-exploitation + nuisance HPs, test-set contamination, loss-spikes-mean-bad-data-pocket) and an Ng 100-misclassified-examples quote under inspect-the-data - refs/llm_judges.md: position/verbosity/self-preference biases (Zheng, Wang 66/80 flip, Panickssery) + mitigation checklist from verdict docs - Lones pitfalls linked as the exhaustive 36-item do/don't checklist - 6 new frozen evidence files; Hamel evals link in further reading Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
32 lines
3.3 KiB
Markdown
32 lines
3.3 KiB
Markdown
# LLM-as-a-judge: known biases and mitigations
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Appendix to the [ML Debugging skill](../SKILL.md). When an LLM-judged eval looks surprisingly good, or a ranking flips between runs, suspect the judge before the model. Each bias below has been measured; verbatim sources in [docs/evidence/llm_judge_biases.md](../docs/evidence/llm_judge_biases.md).
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## The measured biases
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Position bias is large enough to flip rankings outright. Wang et al. (ACL 2024):
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> the quality ranking of candidate responses can be easily hacked by simply altering their order of appearance in the context. [...] e.g., Vicuna-13B could beat ChatGPT on 66 over 80 tested queries with ChatGPT as an evaluator.[^wang]
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Zheng et al. (the MT-Bench paper) named the wider taxonomy: "position, verbosity, and self-enhancement biases, as well as limited reasoning ability"[^zheng]. Their headline agreement number (GPT-4 matches human preference "over 80%", the same as human-human agreement) is the case *for* LLM judges; the bias list is the fine print.
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Self-preference tracks self-recognition. Panickssery et al. fine-tuned models to vary self-recognition ability and found "a linear correlation between self-recognition capability and the strength of self-preference bias"[^panickssery], with controlled experiments supporting a causal reading. A judge that can tell its own outputs apart will favor them, so judging a model with itself (or a sibling checkpoint) is structurally biased.
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There are also output-distribution quirks. From Haize Labs' verdict docs (practitioner notes): the gpt-4o family skews numerical scores upward and mode-collapses even with logprobs; llama-family judges give higher-entropy, more discriminative score distributions; JSON-mode constrained decoding imposes its own inductive bias on scores.[^verdict]
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## Mitigation checklist
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From Wang's calibration framework and verdict's best-practices page:
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- Ask for an explanation or justification *before* the score, not after.
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- Score both orderings and aggregate (Wang's Balanced Position Calibration); at minimum, randomize position and check the flip rate.
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- Use a different model family for the judge (and for any verifier-of-the-judge) than the one being evaluated. Same-model verification produces a positive skew "that may not discriminate faithfully".[^verdict]
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- Inspect the raw score distribution before trusting means: mode collapse or skew means the scale isn't being used.
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- Spot-check judge verdicts against your own reading of ~20 transcripts (the [Ng error-analysis move](../SKILL.md#inspect-the-data-first), applied to the judge).
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- Judge quality is benchmarkable: [JudgeBench](https://huggingface.co/spaces/ScalerLab/JudgeBench) ranks judges on objective-correctness pairs.
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[^zheng]: Zheng et al., "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena" (NeurIPS 2023) — https://arxiv.org/abs/2306.05685
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[^wang]: Wang et al., "Large Language Models are not Fair Evaluators" (ACL 2024) — https://arxiv.org/abs/2305.17926
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[^panickssery]: Panickssery, Bowman, Feng, "LLM Evaluators Recognize and Favor Their Own Generations" (2024) — https://arxiv.org/abs/2404.13076
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[^verdict]: Haize Labs, verdict docs: [best practices](https://verdict.haizelabs.com/docs/best-practices/), [distributional bias cookbook](https://verdict.haizelabs.com/docs/cookbook/distributional-bias/)
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