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This study introduces a multifactor scoring system for evaluating large language model (LLM) responses, addressing the limitations of existing singular evaluation methods. By integrating dimensions such as accuracy, conciseness, factual consistency, readability, and coherence, the framework provides a comprehensive assessment of LLM capabilities. Evaluations on the TruthfulQA dataset reveal that while mainstream LLMs excel in reasoning tasks, they struggle with complex facts and ambiguities, peaking at a composite score of 0.6104.
Mainstream LLMs achieve a peak reasoning score of 0.6104 but falter significantly when faced with complex factual ambiguities.
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.
