Summary of Large Language Models As Agents in Two-player Games, by Yang Liu et al.
Large Language Models as Agents in Two-Player Games
by Yang Liu, Peng Sun, Hang Li
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Large language models (LLMs) have revolutionized natural language processing, but their training processes are often fragmented and unclear. This position paper reconciles the disparate methods of pre-training, fine-tuning, and human feedback-driven reinforcement learning under a single paradigm. By drawing parallels with game theory, we reveal that LLMs can be viewed as agents in language-based games, providing fresh insights into successes and challenges. Our framework reimagines LLM learning processes, shedding light on innovative data preparation techniques and machine learning strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. But they’re not easy to train. This paper brings together different ways of training these models into one simple concept. It shows that training large language models is similar to teaching a game-playing AI, like the kind used in games like poker or chess. By looking at it this way, we can learn new things about how to make these models work better and understand why some things don’t work. |
Keywords
* Artificial intelligence * Fine tuning * Machine learning * Natural language processing * Reinforcement learning
