arXiv:2606.27226v1 Announce Type: new 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
The rapid advancement and widespread adoption of LLMs necessitate more robust, interpretable, and scalable evaluation methods to overcome the limitations of current human and lexical approaches.
Improved LLM evaluation directly impacts the development cycle, trustworthiness, and commercial viability of AI models, accelerating progress and ensuring better alignment with human objectives.
The ability to systematically and interpretably evaluate nuanced LLM output through binary questions allows for more targeted self-improvement and debugging, moving beyond opaque holistic scores.
- · AI developers
- · NLP researchers
- · Companies deploying LLMs
- · AI platform providers
- · Traditional lexical metric developers
- · Opaque LLM evaluation services
More efficient and targeted LLM development cycles reducing time and cost to market for new models and features.
Higher quality and more reliable LLMs across various applications, increasing user trust and adoption.
The democratization of advanced LLM capabilities as evaluation and improvement become less resource-intensive and more accessible.
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Read at arXiv cs.AI