Summary of Reducing the Scope Of Language Models with Circuit Breakers, by David Yunis et al.
Reducing the Scope of Language Models with Circuit Breakers
by David Yunis, Siyu Huo, Chulaka Gunasekara, Danish Contractor
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 concept of scoping in language models, aiming to prevent them from responding to irrelevant or sensitive queries. Current language models are often deployed for specific purposes, but they may still answer questions outside their intended scope. The authors investigate methods for effectively scoping language models, including a recently-proposed method called Circuit Breakers (CB). They demonstrate that CB can be adapted for tasks like sentiment analysis, summarization, and more. Compared to standard methods like fine-tuning or preference learning, CB shows improved performance on out-of-distribution tasks and robustness against adversarial prompting techniques. The authors also show that combining CB with another method called SFT yields the best results: accurate responses to relevant queries while rejecting irrelevant ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure language models only answer questions they’re supposed to, and don’t respond to things like poetry or sensitive company policies. Right now, some language models can get confused and give wrong answers. The authors look at ways to fix this by giving them clear instructions on what to do. They find that a new method called Circuit Breakers works well for specific tasks like analyzing emotions or summarizing news articles. This method is better than usual methods because it’s more accurate and resistant to trick questions. When they combine this method with another one, they get even better results: the language model gives good answers only when asked relevant questions. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Prompting » Summarization
