Summary of Aligning Llms with Individual Preferences Via Interaction, by Shujin Wu et al.
Aligning LLMs with Individual Preferences via Interaction
by Shujin Wu, May Fung, Cheng Qian, Jeonghwan Kim, Dilek Hakkani-Tur, Heng Ji
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 As large language models (LLMs) improve their capabilities, it’s essential to align their behaviors with human values and preferences for widespread adoption. While previous research focuses on general principles like helpfulness and honesty, individual preferences have been overlooked, potentially undermining customized experiences. Our approach trains LLMs to “interact to align” by inferring personalized preferences through multi-turn conversations and dynamically adjusting their responses accordingly. We establish a diverse pool of user personas, leveraging multi-LLM collaboration and reinforcement learning to develop a preference dataset. We evaluate our method using the ALOE benchmark, consisting of 100 examples and well-designed metrics to measure customized alignment performance during conversations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting very good at doing things, but they need to learn what humans want them to do. Right now, these models don’t really understand individual preferences, which is a problem. Imagine you’re having a conversation with a computer program that can understand what you like and dislike, and it adjusts its responses accordingly. That’s basically what our new approach does. We created a big group of different user personas and then trained the language models to talk to each other about these users’ preferences. This helps the models learn how to align their behavior with what individual people want. We tested this method using a special set of examples and found that it works really well. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning
