Evaluation and Comparison of Open-Source LLMs Using Natural Language Generation Quality Metrics
Dzenan Hamzic, Markus Wurzenberger, Florian Skopik, Max Landauer, Andreas Rauber · 2024
The rapid advancement of Large Language Models (LLMs) has transformed natural language processing, yet comprehensive evaluation methods are necessary to ensure their reliability, particularly in Retrieval-Augmented Generation (RAG) tasks. This study aims to evaluate and compare the performance of open-source LLMs by introducing a rigorous evaluation framework. We benchmark 20 LLMs using a combination of established metrics such as BLEU, ROUGE, BERTScore, along with and a novel metric, RAGAS. The models were tested across two distinct datasets to assess their text generation quality. Our findings reveal that models like nous-hermes-2-solar-10.7b and mistral-7b-instruct-v0.1 consistently excel in tasks requiring strict instruction adherence and effective use of large contexts, while other models show areas for improvement. This research contributes to the field by offering a comprehensive evaluation framework that aids in selecting the most suitable LLMs for complex RAG applications, with implications for future developments in natural language processing and big data analysis.