Summary of Evaluating the Fairness Of Task-adaptive Pretraining on Unlabeled Test Data Before Few-shot Text Classification, by Kush Dubey
Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification
by Kush Dubey
First submitted to arxiv on: 30 Sep 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 paper investigates the potential bias in few-shot learning benchmarks for NLP techniques. Researchers use unlabeled test set text to pretrain their models, which might favor methods that easily utilize such data. The authors run experiments to quantify this bias by comparing pretraining on test set text versus independently drawn text. They use 25 classification tasks and three language models (BERT, GPT-2, and Mistral 7B) and find no evidence of overoptimism. Additionally, the study highlights the importance of repeated subsampling in few-shot text classification and recommends including multiple training folds in benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks into a problem with NLP tests that might make some methods seem better than they really are. This could be because researchers use extra data from the test group to train their models. The authors did experiments to see if this is true, using 25 different tasks and three types of language models (BERT, GPT-2, and Mistral 7B). They didn’t find any evidence that some methods are better just because they can use more extra data. The study also shows how important it is to randomly choose small groups from the training data when testing text classification, and recommends doing this multiple times. |
Keywords
» Artificial intelligence » Bert » Classification » Few shot » Gpt » Nlp » Pretraining » Text classification
