Summary of Aligning Large Language Models with Representation Editing: a Control Perspective, by Lingkai Kong et al.
Aligning Large Language Models with Representation Editing: A Control Perspective
by Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, Chao Zhang
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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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 Our research focuses on aligning large language models (LLMs) with human objectives for real-world applications. However, fine-tuning LLMs often faces unstable training and requires substantial computing resources. We propose representation editing to address these challenges. Our method views a pre-trained autoregressive LLM as a discrete-time stochastic dynamical system. To achieve alignment, we introduce external control signals into the state space of this language dynamical system. A value function is trained on hidden states according to the Bellman equation, enabling gradient-based optimization for optimal control signals at test time. Our experiments demonstrate that our method outperforms existing test-time alignment techniques while requiring significantly fewer resources compared to fine-tuning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re trying to make language models work better with what humans want them to do. Right now, it’s hard to get these models to align with human goals because they can be tricky to train and need a lot of computer power. We came up with a new way called representation editing that helps the model understand what humans want. It works by treating the language model like a special kind of math problem and adding control signals to help it make better choices. Our tests show that this method is better than others at getting the job done while using fewer computer resources. |
Keywords
» Artificial intelligence » Alignment » Autoregressive » Fine tuning » Language model » Optimization
