Summary of Autoact: Automatic Agent Learning From Scratch For Qa Via Self-planning, by Shuofei Qiao et al.
AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
by Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Eleanor Jiang, Chengfei Lv, Huajun Chen
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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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 introduces AutoAct, an automatic agent learning framework for question-answering (QA) tasks that does not rely on large-scale annotated data or synthetic planning trajectories from closed-source models. Unlike existing language agent systems, AutoAct synthesizes its own planning trajectories without human assistance and then leverages a division-of-labor strategy to automatically generate sub-agents for completing specific tasks. The framework is tested with different large language models (LLMs) and demonstrates better or parallel performance compared to various strong baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoAct is a new way for computers to answer questions without needing lots of help from humans or special software. It makes its own plans for answering questions, which helps it work well even when there’s not much data available. This computer program can also split tasks into smaller jobs and assign them to different “sub-agents” that are good at specific things. This means AutoAct can answer questions correctly without needing a lot of help from humans. |
Keywords
* Artificial intelligence * Question answering
