FEET: A Framework for Evaluating Embedding Techniques

• URL: http://arxiv.org/abs/2411.01322v1
• Date: Sat, 02 Nov 2024 18:03:49 GMT
• Title: FEET: A Framework for Evaluating Embedding Techniques
• Authors: Simon A. Lee, John Lee, Jeffrey N. Chiang,
• Abstract summary: FEET is a standardized protocol designed to guide the development and benchmarking of foundation models. We define three primary use cases: frozen embeddings, few-shot embeddings, and fully fine-tuned embeddings.
• Score: 0.5837446811360741
• License:
• 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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