Summary of Programming Refusal with Conditional Activation Steering, by Bruce W. Lee et al.
Programming Refusal with Conditional Activation Steering
by Bruce W. Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Erik Miehling, Pierre Dognin, Manish Nagireddy, Amit Dhurandhar
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Conditional Activation Steering (CAST) is a novel approach to precisely control the response behavior of Large Language Models (LLMs). Existing methods indiscriminately alter LLM behavior, limiting their practical applicability in settings where selective responses are essential. CAST analyzes LLM activation patterns during inference to selectively apply or withhold activation steering based on input context. This allows for selective modification of responses to specific content while maintaining normal responses to other content, all without requiring weight optimization. Our method is particularly useful for tasks such as content moderation or domain-specific assistants. We release an open-source implementation of our framework at http://github.com/IBM/activation-steering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to control what Large Language Models (LLMs) say in different situations. Right now, it’s hard to make LLMs give specific answers or not answer at all when they shouldn’t. The researchers came up with a new way called Conditional Activation Steering (CAST) that looks at how the model is thinking during a conversation and decides whether to change its answer based on what’s being said. This helps in situations where you want the model to be more careful, like moderating content online or helping with specific tasks. The team also shared their code so others can use it. |
Keywords
» Artificial intelligence » Inference » Optimization
