The Science of Evaluating Foundation Models
This work addresses the problem of evaluating foundation models for researchers and practitioners in the field of natural language processing, providing an incremental yet crucial step towards more effective model assessment.
This work tackles the challenge of evaluating large foundation models by providing a structured framework and actionable tools, resulting in a more comprehensive evaluation process. The outcome includes a targeted review of advancements in LLM evaluation with an emphasis on real-world applications.
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.