Summary of Label-efficient Model Selection For Text Generation, by Shir Ashury-tahan et al.
Label-Efficient Model Selection for Text Generation
by Shir Ashury-Tahan, Ariel Gera, Benjamin Sznajder, Leshem Choshen, Liat Ein-Dor, Eyal Shnarch
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposed method, DiffUse, efficiently selects between candidate text generation models based on preference annotations. By clustering embeddings representing semantic differences between model outputs, DiffUse identifies a subset of informative examples for making informed decisions. This model-agnostic approach can be applied to any text generation model, prompts, or configurations. The authors demonstrate the effectiveness of DiffUse in reducing annotation requirements by up to 75% while maintaining evaluation reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiffUse is a new way to choose between different text-generating models based on how well they perform. Right now, deciding which model to use can be time-consuming because it requires looking at many examples and saying which one is better. DiffUse makes this process faster by grouping together the examples that show big differences in what the models produce. This helps us pick the best model without needing to look at as many examples. It works with any text-generating model, prompt, or setting. The researchers tested DiffUse with hundreds of different models and showed that it can save a lot of time and effort while still giving reliable results. |
Keywords
* Artificial intelligence * Clustering * Prompt * Text generation
