Integrated Framework for LLM Evaluation with Answer Generation

• URL: http://arxiv.org/abs/2509.20097v2
• Date: Wed, 01 Oct 2025 06:09:25 GMT
• Title: Integrated Framework for LLM Evaluation with Answer Generation
• Authors: Sujeong Lee, Hayoung Lee, Seongsoo Heo, Wonik Choi,
• Abstract summary: We propose an integrated evaluation framework called textitself-refining descriptive evaluation with expert-driven diagnostics, SPEED.<n> SPEED actively incorporates expert feedback across multiple dimensions, including hallucination detection, toxicity assessment, and lexical-contextual appropriateness.<n> Experimental results demonstrate that SPEED achieves robust and consistent evaluation performance across diverse domains and datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable evaluation of large language models is essential to ensure their applicability in practical scenarios. Traditional benchmark-based evaluation methods often rely on fixed reference answers, limiting their ability to capture important qualitative aspects of generated responses. To address these shortcomings, we propose an integrated evaluation framework called \textit{self-refining descriptive evaluation with expert-driven diagnostics}, SPEED, which utilizes specialized functional experts to perform comprehensive, descriptive analyses of model outputs. Unlike conventional approaches, SPEED actively incorporates expert feedback across multiple dimensions, including hallucination detection, toxicity assessment, and lexical-contextual appropriateness. Experimental results demonstrate that SPEED achieves robust and consistent evaluation performance across diverse domains and datasets. Additionally, by employing relatively compact expert models, SPEED demonstrates superior resource efficiency compared to larger-scale evaluators. These findings illustrate that SPEED significantly enhances fairness and interpretability in LLM evaluations, offering a promising alternative to existing evaluation methodologies.

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