Summary of The Real, the Better: Aligning Large Language Models with Online Human Behaviors, by Guanying Jiang et al.
The Real, the Better: Aligning Large Language Models with Online Human Behaviors
by Guanying Jiang, Lingyong Yan, Haibo Shi, Dawei Yin
First submitted to arxiv on: 1 May 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 The abstract proposes a novel alignment framework for large language models (LLMs) to adapt to diverse online human preferences, overcoming limitations in current methods. The Reinforcement Learning with Human Behavior (RLHB) framework uses generative adversarial learning to train LLMs by leveraging real online human behaviors. This approach allows for behavior modeling in natural-language form and multi-model joint training, enabling active and sustainable online alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed RLHB framework can help align LLMs to produce more helpful and less harmful responses. By directly using real online human behaviors, the model learns to generate responses that are more similar to how humans behave online. This approach could lead to more accurate and relevant results in various applications. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning
