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Recommended practices for building a human-annotated set include: sampling by coverage, oversampling failure cases, having two people annotate independently, first measuring human-to-human agreement as an upper bound, adjudicating disagreements into a single gold label, and splitting into a calibration set and a holdout set.
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10/15/2026
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UnverifiedPractices for improving scoring consistency include: prioritizing binary labels in the early stage, using a separate judging prompt per dimension, writing each criterion as a question answerable solely from the image and the description, including pass and fail examples (especially edge cases), and providing the image to the judge.78% similarUnverified在采信LLM裁判评分前必须用人工标注对其进行校准，缓解偏差手段包括交换候选顺序取平均、使用CoT提示、多裁判集成74% similarUnverified在缺乏 review 的环境下，可通过为常见任务建立质量检查清单（数据质量核验、口径一致性、结论与数据匹配等）实现系统化自我审查73% similarUnverified选型建议：应在自己的真实任务上做对比测试，评估代码生成、长文本理解、指令遵循等维度，并综合考量API定价与调用限制，而非仅追逐单次榜单排名73% similarUnverifiedRetrieval and generation must be tested separately; by building a small evaluation set, first check separately whether the retriever recalls the correct evidence, and then evaluate the model's wording.73% similar
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