The Science of Evaluating Foundation Models

TL;DR

This paper proposes a structured framework and practical tools for evaluating large foundation models, addressing challenges of size, diversity of use cases, and ethical considerations in real-world applications.

Contribution

It introduces a formalized evaluation process, actionable tools, and a comprehensive survey of recent advancements in large language model evaluation.

Findings

Structured evaluation framework tailored to use-case contexts

Actionable checklists and templates for reproducible assessments

Survey of recent advancements emphasizing real-world applications

Abstract

The emergent phenomena of large foundation models have revolutionized natural language processing. However, evaluating these models presents significant challenges due to their size, capabilities, and deployment across diverse applications. Existing literature often focuses on individual aspects, such as benchmark performance or specific tasks, but fails to provide a cohesive process that integrates the nuances of diverse use cases with broader ethical and operational considerations. This work focuses on three key aspects: (1) Formalizing the Evaluation Process by providing a structured framework tailored to specific use-case contexts, (2) Offering Actionable Tools and Frameworks such as checklists and templates to ensure thorough, reproducible, and practical evaluations, and (3) Surveying Recent Work with a targeted review of advancements in LLM evaluation, emphasizing real-world applications.