Summary of The Effect Of Fine-tuning on Language Model Toxicity, by Will Hawkins et al.
The effect of fine-tuning on language model toxicity
by Will Hawkins, Brent Mittelstadt, Chris Russell
First submitted to arxiv on: 21 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 A new study investigates how fine-tuning language models affects their propensity to generate toxic content. The researchers assessed three open models – Gemma, Llama, and Phi – using three experiments. They found that small amounts of parameter-efficient fine-tuning on developer-tuned models can significantly alter the results across models. Moreover, they demonstrated how community contributors’ fine-tuning can lead to unpredictable changes in toxicity rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Toxic language is a problem on the internet, and researchers are working to understand why certain AI models produce it. A new study looked at three popular AI models (Gemma, Llama, and Phi) to see if making small changes to them would make them less likely to write mean things. They found that even tiny tweaks can have big effects! It’s like how you might change the way you think about something just by talking to someone new. This study helps us understand why AI models behave in certain ways, and it could lead to better tools for keeping our online conversations kind. |
Keywords
» Artificial intelligence » Fine tuning » Llama » Parameter efficient
