Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks

TL;DR

This paper critically examines the diversity and challenges of current evaluation frameworks for large language models, emphasizing the need for standardized benchmarks to ensure reliable assessment of model capabilities.

Contribution

It offers a comprehensive analysis of existing LLM evaluation methodologies, highlighting their strengths, limitations, and implications for future research.

Findings

Evaluation frameworks vary significantly in scope and methodology

Current benchmarks face challenges in standardization and comparability

Critical insights inform the development of more robust evaluation standards

Abstract

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.