Summary of Feet: a Framework For Evaluating Embedding Techniques, by Simon A. Lee et al.
FEET: A Framework for Evaluating Embedding Techniques
by Simon A. Lee, John Lee, Jeffrey N. Chiang
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A standardized protocol for evaluating foundation models, dubbed FEET, is introduced to guide their development and benchmarking. The protocol focuses on three distinct scenarios: frozen embeddings, few-shot embeddings, and fully fine-tuned embeddings. Two case studies in sentiment analysis and the medical domain demonstrate the effectiveness of this evaluation approach in research applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are being developed to handle various tasks, but it’s hard to know how well they’ll work without testing them. To fix this, researchers created a set called FEET that shows how these models perform in different situations. They looked at three ways the models could be used: keeping their original information, using a little new data, and totally changing what they do based on new info. Two real-life examples show how well this evaluation method works. |
Keywords
* Artificial intelligence * Few shot
