Summary of Star: Sociotechnical Approach to Red Teaming Language Models, by Laura Weidinger et al.
STAR: SocioTechnical Approach to Red Teaming Language Models
by Laura Weidinger, John Mellor, Bernat Guillen Pegueroles, Nahema Marchal, Ravin Kumar, Kristian Lum, Canfer Akbulut, Mark Diaz, Stevie Bergman, Mikel Rodriguez, Verena Rieser, William Isaac
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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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 research introduces STAR, a sociotechnical framework that enhances the safety of large language models by improving red teaming practices. STAR makes two key contributions: it generates parameterized instructions for human red teamers, leading to improved coverage of risk surfaces and more detailed insights into model failures without increasing cost. Additionally, STAR improves signal quality by matching demographics to assess harms for specific groups, resulting in more sensitive annotations. The framework also employs arbitration to leverage diverse viewpoints and improve label reliability, treating disagreement as a valuable contribution to signal quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps keep large language models safe by improving how we test them. It creates a new system called STAR that makes it easier for humans to find and fix problems with these models. STAR gives detailed instructions to help testers cover more areas of risk and understand what’s going wrong. It also matches the model’s performance to specific groups, making sure it doesn’t harm anyone unfairly. By working together and considering different opinions, STAR helps make the labels we use more reliable. |
