# Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System

arXiv: https://arxiv.org/abs/2607.06940 · HTML: https://mlsanity.com/paper/2607.06940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2026.

## Abstract (arXiv)

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.