Summary of Input Conditioned Graph Generation For Language Agents, by Lukas Vierling et al.
Input Conditioned Graph Generation for Language Agents
by Lukas Vierling, Jie Fu, Kai Chen
First submitted to arxiv on: 17 Jun 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 paper presents a novel approach to developing dynamic language agents, which can adjust their internal communication based on input. The authors use a graph framework to represent language agents and train a Large Language Model (LLM) using reinforcement learning to generate edges that represent the flow of communication. This allows the agent to adapt to different domains during training, achieving good performance when encountering new data. The paper demonstrates that this approach outperforms traditional static approaches by nearly 6% accuracy on a combined dataset and over 10% on others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research aims to make language agents smarter and more flexible. By creating an internal communication system that adjusts based on input, the agent can better understand different types of data. The authors use a special kind of artificial intelligence called a Large Language Model to train their agent. This allows it to learn from many different datasets at once and adapt to new situations. |
Keywords
» Artificial intelligence » Large language model » Reinforcement learning
