@HamelHusain
Yes! binary judges are far more practical for most people, because likert scales (or scores) have too many footguns All the flashcards are here (inspired by @chrisalbon ‘s flashcards) https://t.co/qfB4WJgX5n https://t.co/OvSdVi5rbB
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"text": "If you use LLM-as-judge, this one is worth reading.\n\n(bookmark it)\n\nIt's actually one of the most effective ways to use LLM-as-a-Judge for evals.\n\nHolistic judge scores hide both their reasoning and their ceiling effects.\n\nBINEVAL decomposes each evaluation criterion into atomic yes-or-no questions, answers each independently per output, then aggregates the verdicts into calibrated multi-dimensional scores.\n\nEvery question-level verdict is inspectable, so you can diagnose exactly why an output scored low, and the same verdicts feed straight back as targeted prompt-improvement signal.\n\nAcross SummEval, Topical-Chat, and QAGS, it matches or beats UniEval and G-Eval, training-free, with especially strong results on factual consistency.\n\nPaper: https://t.co/oar6BZcasm\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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