[2606.27226] Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
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Computer Science > Artificial Intelligence
arXiv:2606.27226 (cs)
[Submitted on 25 Jun 2026]
Title:Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Authors:Sangwoo Cho, Kushal Chawla, Pengshan Cai, Zefang Liu, Chenyang Zhu, Shi-Xiong Zhang, Sambit Sahu<br>View a PDF of the paper titled Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement, by Sangwoo Cho and 6 other authors
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Abstract: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.
Comments:<br>Acceepted to the Second Workshop on Compositional Learning at ICML 2026, Seoul, South Korea
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as:<br>arXiv:2606.27226 [cs.AI]
(or<br>arXiv:2606.27226v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.27226
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arXiv-issued DOI via DataCite (pending registration)
Submission history<br>From: Sangwoo Cho [view email]<br>[v1]<br>Thu, 25 Jun 2026 16:14:50 UTC (603 KB)
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