Summary of Understanding the Interplay Of Scale, Data, and Bias in Language Models: a Case Study with Bert, by Muhammad Ali et al.
Understanding the Interplay of Scale, Data, and Bias in Language Models: A Case Study with BERT
by Muhammad Ali, Swetasudha Panda, Qinlan Shen, Michael Wick, Ari Kobren
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper investigates the impact of scaling up language models like BERT on their social biases and stereotyping tendencies. The researchers focus on four architecture sizes of BERT, exploring how pre-training data influences biases during both upstream language modeling and downstream classification tasks. They find that larger models pre-trained on large internet datasets exhibit higher toxicity, while those trained on moderated data sources like Wikipedia show greater gender stereotypes. However, downstream biases decrease with increasing model scale, regardless of the pre-training data. This study highlights the role of pre-training data in shaping biased behavior, a often overlooked aspect in the study of scaling up language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how making language models bigger affects their social biases. They test four versions of BERT to see if larger models become more or less biased when trained on different kinds of data. The results show that bigger models trained on lots of internet data are more toxic, while those trained on Wikipedia are more sexist. But once these models are used for specific tasks, the bias goes down no matter how big the model is. This study shows why it’s important to think about where our language models get their training data. |
Keywords
» Artificial intelligence » Bert » Classification
