Open LLMs with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs' agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://github.com/THUDM/AgentTuning, serving open and powerful alternatives to commercial LLMs for agent tasks.
The paper introduces AgentTuning, a novel approach to enhance the agent capabilities of LLMs through a lightweight dataset (AgentInstruct) and a hybrid instruction-tuning strategy.
The research was evaluated on the Llama 2 series, resulting in the creation of AgentLM, which demonstrated competitive performance with commercial models like GPT-3.5-turbo on sophisticated agent tasks.
AgentInstruct and the resulting models (AgentLM-7B, 13B, 70B) are open-source, providing robust alternatives to commercial LLMs for various agent tasks, maintaining general abilities without compromise.
AgentTuning: Enabling Generalized Agent Abilities for LLMs presents a novel approach designed to enhance the agent capabilities of LLMs. This work introduces two key components: AgentInstruct, a lightweight instruction-tuning dataset, and a hybrid instruction-tuning strategy combining agent-specific data with general-domain instructions. The primary goal of this research is to improve the agent capabilities of LLMs without sacrificing their general utility. The proposed method was evaluated on the Llama 2 series, resulting in the creation of AgentLM, which demonstrated competitive performance with commercial models like GPT-3.5-turbo on sophisticated agent tasks.
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AgentInstruct Construction:
Hybrid Instruction-Tuning Strategy:
AgentLM's Practical Implications:
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AgentTuning: Enabling Generalized Agent Abilities for LLMs offers a significant contribution to the field by providing a method to enhance the agent capabilities of LLMs without compromising their general abilities. By introducing AgentInstruct and a hybrid instruction-tuning strategy, the research demonstrates the feasibility of achieving robust agent performance comparable to commercial models using open-source LLMs. This work not only bridges the gap between open-source and commercial models but also facilitates further research and development in the domain of autonomous agents.