As large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Evaluating the performance of these models is a complex challenge that requires careful consideration of various linguistic tasks, model architectures, and benchmarking methodologies. In recent years, various frameworks have emerged as noteworthy contributions to the field, offering comprehensive evaluation tests and benchmarks for assessing the capabilities of LLMs across diverse domains. This paper provides an exploration and critical analysis of some of these evaluation methodologies, shedding light on their strengths, limitations, and impact on advancing the state-of-the-art in natural language processing.
@article{arxiv.2407.21072,
title = {Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks},
author = {Marco AF Pimentel and Clément Christophe and Tathagata Raha and Prateek Munjal and Praveen K Kanithi and Shadab Khan},
journal= {arXiv preprint arXiv:2407.21072},
year = {2024}
}