Summary of Agentgen: Enhancing Planning Abilities For Large Language Model Based Agent Via Environment and Task Generation, by Mengkang Hu et al.
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
by Mengkang Hu, Pu Zhao, Can Xu, Qingfeng Sun, Jianguang Lou, Qingwei Lin, Ping Luo, Saravan Rajmohan
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper investigates enhancing the planning abilities of Large Language Model (LLM)-based agents through instruction tuning, referred to as agent training. The authors demonstrate that utilizing expert-level trajectories for instruction-tuning LLMs effectively enhances their planning capabilities. However, existing work primarily focuses on synthesizing trajectories from manually designed planning tasks and environments. To address this limitation, the paper introduces a framework, AgentGen, that leverages LLMs to generate diverse environments and planning tasks. The authors propose using an inspiration corpus of domain-specific text segments as context for synthesizing environments and a bidirectional evolution method, Bi-Evol, to evolve planning tasks from easier and harder directions. Evaluation results show that AgentGen greatly improves LLMs’ planning ability, surpassing GPT-3.5 in overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us make better language models that can plan things out! It’s like teaching a super smart AI how to solve problems. The authors are trying to find new ways to teach these language models so they can get even better at planning. They’re using something called “instruction tuning” and making it more automatic by letting the language model generate its own environments and tasks. This could help make language models that are really good at solving complex problems. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning » Language model » Large language model
