Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance

• URL: http://arxiv.org/abs/2410.12361v1
• Date: Wed, 16 Oct 2024 08:24:09 GMT
• Title: Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
• Authors: Yaxi Lu, Shenzhi Yang, Cheng Qian, Guirong Chen, Qinyu Luo, Yesai Wu, Huadong Wang, Xin Cong, Zhong Zhang, Yankai Lin, Weiwen Liu, Yasheng Wang, Zhiyuan Liu, Fangming Liu, Maosong Sun,
• Abstract summary: We tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions. First, we collect real-world human activities to generate proactive task predictions. These predictions are labeled by human annotators as either accepted or rejected. The labeled data is used to train a reward model that simulates human judgment.
• Score: 95.03771007780976
• License:
• Abstract: Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scenarios requiring foresight and autonomous decision-making. In this paper, we tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions. We propose a novel data-driven approach for this problem. Firstly, we collect real-world human activities to generate proactive task predictions. These predictions are then labeled by human annotators as either accepted or rejected. The labeled data is used to train a reward model that simulates human judgment and serves as an automatic evaluator of the proactiveness of LLM agents. Building on this, we develop a comprehensive data generation pipeline to create a diverse dataset, ProactiveBench, containing 6,790 events. Finally, we demonstrate that fine-tuning models with the proposed ProactiveBench can significantly elicit the proactiveness of LLM agents. Experimental results show that our fine-tuned model achieves an F1-Score of 66.47% in proactively offering assistance, outperforming all open-source and close-source models. These results highlight the potential of our method in creating more proactive and effective agent systems, paving the way for future advancements in human-agent collaboration.

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