Summary of Controllable Preference Optimization: Toward Controllable Multi-objective Alignment, by Yiju Guo et al.
Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment
by Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to artificial intelligence alignment is proposed, focusing on the consistency between model responses and human preferences. The “alignment tax” arises when enhancements in one objective compromise performance in others. Existing techniques are largely unidirectional, leading to suboptimal trade-offs. To overcome this challenge, grounding large language models (LLMs) with explicit preferences is crucial. Controllable preference optimization (CPO) is introduced, specifying preference scores for different objectives and guiding the model to generate responses that meet requirements. Experimental results show aligned models can provide responses matching various “3H” desiderata (helpfulness, honesty, harmlessness). By introducing diverse data and alignment goals, CPO surpasses baseline methods in single-objective alignment, mitigating the alignment tax and achieving improvements in multi-objective alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence tries to make sure AI models think like humans do. This is called “alignment.” But, when we make one thing better, it can make something else worse. This is called the “alignment tax.” Some ways we try to align AI models are not very good at handling multiple goals at once. To fix this, we need to tell the AI model what’s important and what’s not. We call this technique “controllable preference optimization” (CPO). CPO lets us choose which things are most important and helps the AI model make decisions that match our preferences. |
Keywords
» Artificial intelligence » Alignment » Grounding » Optimization
