Summary of Bias Amplification: Large Language Models As Increasingly Biased Media, by Ze Wang et al.
Bias Amplification: Large Language Models as Increasingly Biased Media
by Ze Wang, Zekun Wu, Jeremy Zhang, Xin Guan, Navya Jain, Skylar Lu, Saloni Gupta, Adriano Koshiyama
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 phenomenon of bias amplification in Large Language Models (LLMs), where preexisting social biases are reinforced over time. It defines the conditions under which this occurs and demonstrates through statistical simulations that it can happen even without sampling errors, the primary driver of model collapse. The authors then empirically test political bias amplification in GPT2 using a custom-built benchmark for sentence continuation tasks and find a progressively increasing right-leaning bias. They also evaluate three mitigation strategies and show that bias amplification persists even when model collapse is mitigated. Finally, they provide a mechanistic interpretation of the results, suggesting distinct sets of neurons are responsible for model collapse and bias amplification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how Large Language Models can get worse at predicting certain things because they’re trained on fake data. This is called “model collapse”. But what’s new here is that it also finds out how this makes the models more biased towards one political side or another. The authors use a special test to see if this happens and find that it does, even when they try to fix the problem of model collapse. They also figure out which parts of the model are making things worse. |
