Summary of Tradeoffs Between Alignment and Helpfulness in Language Models with Representation Engineering, by Yotam Wolf et al.
Tradeoffs Between Alignment and Helpfulness in Language Models with Representation Engineering
by Yotam Wolf, Noam Wies, Dorin Shteyman, Binyamin Rothberg, Yoav Levine, Amnon Shashua
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper investigates the tradeoff between language model alignment and helpfulness when using representation engineering, a method that modifies a pre-trained language model’s behavior by altering its representations post-training. The authors propose a theoretical framework to bound these two quantities and demonstrate their relevance empirically. They find that alignment can be guaranteed with representation engineering, but at the cost of decreased helpfulness, which is harmed quadratically with the norm of the representation engineering vector. In contrast, alignment increases linearly with it, indicating an efficient regime for using representation engineering. The authors validate their findings and chart the boundaries to the usefulness of representation engineering for alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are getting smarter, but we need to make sure they’re safe to use around humans. One way to do this is by “aligning” them so they behave well and don’t say or do anything bad. Researchers have found that a technique called representation engineering can help align language models, making them less likely to spread misinformation or be biased against certain groups. However, this technique also makes the model worse at doing simple tasks like answering questions. The authors of this paper want to know how much we need to sacrifice helpfulness to get alignment. They came up with a way to measure these two things and found that as we make the language model more aligned, it gets less useful. But they also showed that there’s an “optimal” amount of alignment we can achieve while still keeping the model somewhat helpful. |
Keywords
* Artificial intelligence * Alignment * Language model
