BINEVAL: A Binary Question Framework for Interpretable LLM Evaluation and Self-Improvement
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[Submitted on 25 Jun 2026]
Summary
This paper introduces BINEVAL, a framework for evaluating LLM outputs that decomposes evaluation criteria into atomic binary questions. Instead of using opaque holistic scores, BINEVAL generates fine-grained yes/no questions via a meta-prompt, answers them independently for each output, and aggregates verdicts into interpretable multi-dimensional scores. The framework matches or outperforms strong baselines like UniEval and G-Eval on benchmarks including SummEval, Topical-Chat, and QAGS, with particularly strong results on factual consistency. BINEVAL also supports iterative prompt optimization through its transparent question-level feedback, enabling both self-update and cross-model update settings. The authors position it as a task-agnostic, training-free, and interpretable evaluation framework with practical diagnostic value.
Source
Twitter / XBINEVAL: A Binary Question Framework for Interpretable LLM Evaluation and Self-Improvementarxiv.orgKey quotes
· 5 pulledEvaluating 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.
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.
Overall, BINEVAL provides a task-agnostic, training-free, and interpretable evaluation framework that combines strong empirical performance with practical diagnostic and optimization value.
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