Summary of Aligning Large Language Models Via Fine-grained Supervision, by Dehong Xu et al.
Aligning Large Language Models via Fine-grained Supervision
by Dehong Xu, Liang Qiu, Minseok Kim, Faisal Ladhak, Jaeyoung Do
First submitted to arxiv on: 4 Jun 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 Medium Difficulty summary: This paper explores methods for enhancing the alignment between pre-trained large-scale language models (LLMs) and user expectations. The current approach uses reinforcement learning with human feedback (RLHF), which works by transforming coarse human preferences into a feedback signal that guides the model’s learning process. However, this method lacks precision in identifying the exact parts of the output affecting user preferences. To address this gap, the authors propose a method to enhance LLM alignment through fine-grained token-level supervision. This involves annotators minimally editing less preferred responses within the standard reward modeling dataset to make them more favorable. The refined dataset is then used to train a token-level reward model, which is used for training a Proximal Policy Optimization (PPO) model. Experimental results demonstrate that this approach can achieve up to an absolute improvement of 5.1% in LLM performance compared with the traditional PPO model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper looks at ways to make large language models better match what people want. Right now, these models are good at writing articles that sound natural, but they might not always be true or nice. The current method to fix this uses feedback from humans to train the models. However, this approach has some limitations. To overcome these limitations, the authors propose a new way of training the models using small changes to the model’s output. This involves asking people to make slight adjustments to the model’s responses to make them more favorable. The results show that this new method can improve the model’s performance by up to 5.1% compared to the traditional approach. |
Keywords
» Artificial intelligence » Alignment » Optimization » Precision » Reinforcement learning » Rlhf » Token
