Summary of Sequence to Sequence Reward Modeling: Improving Rlhf by Language Feedback, By Jiayi Zhou et al.
Sequence to Sequence Reward Modeling: Improving RLHF by Language Feedback
by Jiayi Zhou, Jiaming Ji, Juntao Dai, Yaodong Yang
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 research paper tackles the critical challenge of aligning Large Language Models (LLMs) with human intentions and values. The authors explore Reinforcement Learning from Human Feedback (RLHF), a popular method that trains reward models on human preferences and fine-tunes LLMs to maximize feedback. However, RLHF is prone to biased local optimization, leading to unexpected generalizations and misalignment with objectives. To address this issue, the paper proposes a novel sequence-to-sequence (seq2seq) reward modeling method that leverages language feedback instead of scalar feedback. This approach enables richer, fine-grained language feedback without additional annotations or training stages. The authors demonstrate the effectiveness of seq2seq RM in reducing biases in single-turn safety dialogues and text summarization tasks, achieving an average win rate of 76.9% across various LLMs and NLP tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making sure that AI language models behave the way humans want them to. The authors are trying to fix a problem with current methods that trains these models using human feedback, but it can sometimes lead to unexpected results. They propose a new approach called sequence-to-sequence reward modeling that uses language feedback instead of just numbers. This helps the model learn more accurately and reduces biases in its responses. The authors tested this method on different tasks and showed that it can improve the performance of AI models, making them more useful for applications like chatbots and text summarization. |
Keywords
» Artificial intelligence » Nlp » Optimization » Reinforcement learning from human feedback » Rlhf » Seq2seq » Summarization
