Summary of Stronger Random Baselines For In-context Learning, by Gregory Yauney and David Mimno
Stronger Random Baselines for In-Context Learning
by Gregory Yauney, David Mimno
First submitted to arxiv on: 19 Apr 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 study evaluates the in-context learning classification performance of language models, addressing challenges posed by small dataset sizes and intentional difficulty. The researchers introduce a stronger random baseline that accounts for validation set reuse and existing small datasets, providing a more accurate predictor of held-out performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about how well language models can learn from limited information. It’s like trying to guess what someone wrote based on just a few words they gave you. The researchers wanted to make sure their tests weren’t too easy or too hard, so they came up with a new way to measure how well the models do. They found that many of the results that seemed impressive at first didn’t actually mean much, and that the new method is a better way to predict how well the models will do in real-life situations. |
Keywords
» Artificial intelligence » Classification
