The Science of Evaluating Foundation Models

TL;DR

The paper tackles the challenge of evaluating foundation models across diverse use cases by proposing the ABCD in Evaluation framework, which integrates Algorithm, Big Data, Computing Resources, and Domain Expertise to structure assessments. It outlines a multidimensional evaluation scheme covering performance, robustness, ethics and fairness, explainability, and safety, and provides actionable tools such as checklists and documentation templates to enable thorough, reproducible evaluations. The authors survey recent LLM evaluation work, highlighting benchmarks, metrics, and automated tools, while emphasizing limitations in domain specificity, dynamic environments, and the need for multi-agent governance approaches. They advocate for domain-aware, iterative evaluation processes with transparent documentation to adapt to evolving capabilities and societal considerations, aiming to yield more robust, fair, and trustworthy AI systems.

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.

Figures (3)

• Figure 1: Workflow of evaluation
• Figure 2: Dimensions of Evaluation
• Figure 3: Evaluation Methodologies