Summary of Alarm: Align Language Models Via Hierarchical Rewards Modeling, by Yuhang Lai et al.
ALaRM: Align Language Models via Hierarchical Rewards Modeling
by Yuhang Lai, Siyuan Wang, Shujun Liu, Xuanjing Huang, Zhongyu Wei
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper introduces ALaRM, a novel framework that addresses the limitations of current reinforcement learning from human feedback (RLHF) approaches by modeling hierarchical rewards. Specifically, ALaRM integrates holistic and aspect-specific rewards to provide more precise and consistent guidance for large language models (LLMs). This approach is designed to enhance the alignment of LLMs with human preferences, which is crucial in complex text generation tasks. The authors employ a methodology that filters and combines multiple rewards based on their consistency, providing a reliable mechanism for improving model alignment. ALaRM is validated through applications in long-form question answering and machine translation tasks, outperforming existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to make large language models (LLMs) work more like humans. Right now, these models are trained using rewards that don’t always match what humans want them to do. To fix this, the authors created ALaRM, a new way of training LLMs using hierarchical rewards. This means combining different types of rewards to give the model more consistent guidance. The authors tested their approach in two tasks: generating long answers and translating text from one language to another. They found that ALaRM worked better than other methods. |
Keywords
* Artificial intelligence * Alignment * Question answering * Reinforcement learning from human feedback * Rlhf * Text generation * Translation
