Summary of From Representational Harms to Quality-of-service Harms: a Case Study on Llama 2 Safety Safeguards, by Khaoula Chehbouni et al.
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
by Khaoula Chehbouni, Megha Roshan, Emmanuel Ma, Futian Andrew Wei, Afaf Taik, Jackie CK Cheung, Golnoosh Farnadi
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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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 explores the challenges and limitations of large language models (LLMs) in ensuring safety and mitigating biases. Despite advancements in LLMs, concerns persist regarding their potential negative impact on marginalized populations. The authors investigate the effectiveness of existing safety measures by evaluating LLMs optimized for safety. They use the case study of Llama 2 to demonstrate how these models can still encode harmful assumptions even with mitigation efforts in place. The researchers create a taxonomy of LLM responses to users, revealing pronounced trade-offs between safety and helpfulness, particularly for certain demographic groups, which can lead to quality-of-service harms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at the problems with big language models (LLMs) that help computers understand human language. Even though these models are getting better, they still have some big issues. One of the main problems is that they might not be very good for people who are already having a tough time. The authors want to know if the ways we’re trying to make these models safer really work. They use one example, Llama 2, to show that even when we try to make them safe, they can still have some big problems. They also create a way to understand how these models respond to people, and what they found is that it’s not always good for everyone. |
Keywords
* Artificial intelligence * Llama
