FEET: A Framework for Evaluating Embedding Techniques

Simon A. Lee; John Lee; Jeffrey N. Chiang

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arXiv · 2411.01322

FEET: A Framework for Evaluating Embedding Techniques

Simon A. Lee·John Lee·Jeffrey N. Chiang

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Abstract

In this study, we introduce FEET, a standardized protocol designed to guide the development and benchmarking of foundation models. While numerous benchmark datasets exist for evaluating these models, we propose a structured evaluation protocol across three distinct scenarios to gain a comprehensive understanding of their practical performance. We define three primary use cases: frozen embeddings, few-shot embeddings, and fully fine-tuned embeddings. Each scenario is detailed and illustrated through two case studies: one in sentiment analysis and another in the medical domain, demonstrating how these evaluations provide a thorough assessment of foundation models' effectiveness in research applications. We recommend this protocol as a standard for future research aimed at advancing representation learning models.

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Simon A. Lee, John Lee, Jeffrey N. Chiang. 2024-11-02. FEET: A Framework for Evaluating Embedding Techniques. https://arxiv.org/abs/2411.01322

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