Summary of When Llms Are Unfit Use Fastfit: Fast and Effective Text Classification with Many Classes, by Asaf Yehudai et al.
When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes
by Asaf Yehudai, Elron Bendel
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 The paper introduces FastFit, a novel method and Python package designed to provide fast and accurate few-shot classification, particularly for scenarios with many semantically similar classes. By integrating batch contrastive learning and token-level similarity scores, FastFit outperforms existing few-shot learning packages like SetFit, Transformers, or large language models via API calls in multiclass classification performance on the FewMany and Multilingual datasets. The package achieves a 3-20x improvement in training speed, completing training in just a few seconds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FastFit is a new way to classify things really fast and accurately. It’s good at handling lots of classes that are similar. This method combines two techniques: batch contrastive learning and token-level similarity scores. This makes it better than other methods like SetFit or using big language models through APIs. FastFit works well on different datasets, including FewMany and Multilingual. It’s also really fast, taking just a few seconds to train. |
Keywords
» Artificial intelligence » Classification » Few shot » Token
