BINEVAL把LLM评估拆成一堆“是/否”问题，结果好理解、易调试，在事实一致性上比UniEval还准，还能自己优化提示词。
BINEVAL将评估标准拆解为原子化的二元问题，由LLM独立回答后聚合为多维可解释分数。在SummEval、Topical-Chat和QAGS三个基准上，BINEVAL匹配或超越UniEval和G-Eval，尤其擅长事实一致性评估（QAGS上的表现突出）。其问题级反馈可用于迭代优化评估提示和生成提示，在IFBench的自我更新和跨模型更新设置中均有效。该方法无需训练、任务无关，且避免了现有LLM评判器的天花板效应。
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug. We propose BINEVAL, a framework that decomposes evaluation criteria into atomic binary questions and aggregates the resulting verdicts into interpretable, multi-dimensional scores. Given a task prompt, a meta-prompt generates fine-grained evaluation questions, and an LLM answers them independently for each output, yielding transparent question-level feedback together with calibrated overall scores. This decomposition makes evaluation easier to inspect, easier to diagnose, and directly usable for prompt improvement. Across SummEval, Topical-Chat, and QAGS, BINEVAL matches or outperforms strong baselines including UniEval and G-Eval, with especially strong results on factual consistency benchmarks such as QAGS. Beyond competitive correlation with human judgments, BINEVAL better matches human score distributions and avoids the ceiling effects common in prior LLM judges, leading to better discrimination between borderline and clearly flawed outputs. We further show that the same question-level feedback supports iterative prompt optimization, improving evaluator prompts on summarization and generation prompts on IFBench under both self-update and cross-model update settings. Overall, BINEVAL provides a task-agnostic, training-free, and interpretable evaluation framework that combines strong empirical performance with practical diagnostic and optimization value.