Summary of Knowagent: Knowledge-augmented Planning For Llm-based Agents, by Yuqi Zhu et al.
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
by Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
First submitted to arxiv on: 5 Mar 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 Large Language Models (LLMs) have shown impressive results on complex reasoning tasks, but they struggle when interacting with environments through executable actions. This limitation stems from the lack of built-in action knowledge in language agents, leading to planning hallucinations. To address this issue, KnowAgent is introduced, a novel approach that enhances the planning capabilities of LLMs by incorporating explicit action knowledge. KnowAgent employs an action knowledge base and self-learning strategy to constrain the action path during planning, enabling more reasonable trajectory synthesis. Experimental results on HotpotQA and ALFWorld demonstrate comparable or superior performance to existing baselines. Further analysis shows the effectiveness of KnowAgent in mitigating planning hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Large Language Models (LLMs) better at solving problems by giving them a “brain” that understands actions. Right now, LLMs are great at understanding language, but they struggle when it comes to taking actual actions in the world. To fix this, the authors created a new approach called KnowAgent that helps LLMs make better plans for what actions to take. They tested KnowAgent on two different tasks and found that it works really well! This is important because it could help LLMs be more useful in real-world situations. |
Keywords
* Artificial intelligence * Knowledge base
