Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System
Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System
大语言模型回复的综合评估：一种多因素评分系统
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. 摘要： 大语言模型（LLMs）在语言任务中的卓越表现，凸显了对其回复质量进行全面评估的迫切需求。目前主流的评估方法往往局限于单一维度，难以全面捕捉模型能力的各个方面。
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. 本研究引入了一种多因素评分范式，整合了准确性、简洁性、事实一致性、可读性和连贯性，并辅以图形用户界面（GUI）来可视化评估结果。
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. 通过在 TruthfulQA 数据集上的评估发现，主流大语言模型在推理任务中表现出色（综合得分最高达到 0.6104），但在处理复杂事实和歧义问题时仍存在普遍的局限性。
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. 该框架超越了传统指标的狭隘视角，提供了一种透明且灵活的途径，用以阐明模型的潜力和不足。尽管目前主要聚焦于英语任务，但其未来前景广阔，有望扩展至多语言领域。这项工作为知识工程和模型优化开辟了一条新路径。
Paper Details:
- Authors: Yiming Gai, Junde Lu, Xuefei Huang
- arXiv ID: 2607.06940
- Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
- Submission Date: 8 Jul 2026
论文详情：
- 作者： Yiming Gai, Junde Lu, Xuefei Huang
- arXiv ID: 2607.06940
- 学科分类： 计算与语言 (cs.CL)；人工智能 (cs.AI)
- 提交日期： 2026年7月8日