Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System
A new multi-factor scoring framework integrates five dimensions to evaluate LLM response quality, revealing strengths in reasoning but significant weaknesses in factual consistency and ambiguity handling.
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[Submitted on 8 Jul 2026]
Title:Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System
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Abstract:The remarkable performance of large language models (LLMs) in linguistic tasks underscores an urgent need for comprehensive evaluation of their response quality. Prevailing methods, often confined to singular dimensions, fall short of capturing the full spectrum of model capabilities. This study introduces a multifactor scoring paradigm, integrating accuracy, conciseness, factual consistency, readability, and coherence, complemented by a graphical user interface (GUI) for visualizing outcomes. Evaluations on the TruthfulQA dataset unveil mainstream LLMs' strengths in reasoning tasks (peaking at a composite score of 0.6104) alongside pervasive limitations in navigating complex facts and ambiguities. Transcending the narrow lens of traditional metrics, this framework offers a transparent, adaptable avenue to illuminate model potential and deficiencies. Though presently focused on English tasks, its horizons beckon toward multilingual domains. This work carves a novel path for knowledge engineering and model refinement.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.06940 [cs.CL]
(or arXiv:2607.06940v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.06940
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yiming Gai [view email] [v1] Wed, 8 Jul 2026 03:10:33 UTC (1,508 KB)
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