Summary of Sudolm: Learning Access Control Of Parametric Knowledge with Authorization Alignment, by Qin Liu et al.
SudoLM: Learning Access Control of Parametric Knowledge with Authorization Alignment
by Qin Liu, Fei Wang, Chaowei Xiao, Muhao Chen
First submitted to arxiv on: 18 Oct 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 The proposed framework, SudoLM, addresses limitations in existing preference alignment mechanisms by introducing authorization alignment, enabling large language models (LLMs) to learn access control over specific parametric knowledge based on user credentials. This allows authorized users to unlock access to relevant information while blocking it for non-qualified individuals. The approach is evaluated through experiments in two application scenarios, demonstrating its effectiveness in controlling access and maintaining general utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SudoLM is a new way to help big language models share information with people who need it. Right now, these models are set up so that anyone can get all the information, even if they’re not qualified to handle it. This isn’t good for experts who want access to specific knowledge. SudoLM lets the model learn how to decide what information each person can see based on their credentials. Users with special keys can unlock more advanced information, while others are blocked from seeing it. |
Keywords
» Artificial intelligence » Alignment
