Summary of Direct Language Model Alignment From Online Ai Feedback, by Shangmin Guo et al.
Direct Language Model Alignment from Online AI Feedback
by Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, Mathieu Blondel
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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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 study introduces Online AI Feedback (OAIF), a method that improves Direct Alignment from Preferences (DAP) techniques by incorporating online feedback during training. Unlike traditional DAP methods, which rely on offline preference datasets collected ahead of time, OAIF uses a large language model as an annotator to provide real-time feedback. The LLM is prompted to choose between two responses generated by the current model, allowing for fine-tuning and adaptation during training. Experimental results demonstrate that OAIF outperforms both offline DAP and reinforcement learning from human feedback (RLHF) methods in several tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new way to make AI models better at understanding what people want. They called it Online AI Feedback (OAIF). Instead of using old data collected ahead of time, OAIF uses a super smart language model as an editor to help the AI learn what’s good and bad. The editor looks at two responses from the AI and chooses which one is better. This helps the AI get better really fast! The scientists tested it and found that OAIF works better than other methods. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Language model » Large language model » Reinforcement learning from human feedback » Rlhf
