KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (AI summary)

Key Points

1. LLM-based agents exhibit shortcomings in handling complex reasoning tasks and planning in open environments due to a lack of built-in action knowledge.

2. The proposed K NOWAGENT framework aims to enhance the planning capabilities of LLMs by incorporating explicit action knowledge through an action knowledge base and a knowledgeable self-learning strategy.

3. Experimental results on HotpotQA and ALFWorld demonstrate that K NOWAGENT can achieve comparable or superior performance to existing baselines and effectively mitigate planning hallucinations.

4. K NOWAGENT leverages external action knowledge to guide the planning trajectory and address planning hallucinations by restraining the action path during planning.

5. The approach involves the conversion of action knowledge into text format to facilitate subsequent operations and a knowledgeable self-learning phase for continual model improvement.

6. Results show that K NOWAGENT outperforms various prompt-based methods and other baselines, emphasizing the effectiveness of incorporating action knowledge for planning purposes.

7. The study highlights the impact of iterative training in enhancing model proficiency and the role of action knowledge in mitigating planning hallucinations.

8. K NOWAGENT's effectiveness is demonstrated through statistical rates of invalid and misordered actions, and comparisons between manually designed and distilled knowledge bases.

9. The paper concludes by discussing potential directions for expanding K NOWAGENT's applicability, exploring multi-agent systems, and automating the design of action knowledge bases.

Summary

The research paper introduces KNOWAGENT, a novel approach designed to enhance the planning capabilities of Large Language Models (LLMs) by incorporating explicit action knowledge. The paper highlights the inadequacy of LLMs in dealing with sophisticated challenges, particularly in generating executable actions, due to the lack of built-in action knowledge, which leads to planning hallucination. To address this issue, KNOWAGENT employs an action knowledge base and a knowledgeable self-learning strategy to constrain the action path during planning, resulting in enhanced planning performance. Experimental results on HotpotQA and ALFWorld show that KNOWAGENT achieves comparable or superior performance to existing baselines and effectively mitigates planning hallucinations.

The paper presents the methodology behind KNOWAGENT's design, including the incorporation of action knowledge, the conversion of action knowledge to text, and the knowledgeable self-learning phase. The effectiveness of KNOWAGENT is supported by experiments across various models, demonstrating its competitiveness with or superiority to other baselines. The study also identifies the potential for KNOWAGENT's application in diverse fields and suggests future research directions, including the exploration of multi-agent systems and automated design of action knowledge bases to enhance the model's autonomy and versatility.

In conclusion, the paper introduces a novel approach, KNOWAGENT, which effectively enhances the planning capabilities of LLM-based language agents by incorporating external action knowledge, leading to improved performance and mitigated planning hallucinations. The experimental results demonstrate the effectiveness of KNOWAGENT across various models and datasets, paving the way for potential applications in a wide range of fields and suggesting future research opportunities.

Reference: https://arxiv.org/abs/2403.031...