  Open-source pre-trained Large Language Models (LLMs) exhibit strong language
understanding and generation capabilities, making them highly successful in a
variety of tasks. However, when used as agents for dealing with complex
problems in the real world, their performance is far inferior to large
commercial models such as ChatGPT and GPT-4. As intelligent agents, LLMs need
to have the capabilities of task planning, long-term memory, and the ability to
leverage external tools to achieve satisfactory performance. Various methods
have been proposed to enhance the agent capabilities of LLMs. On the one hand,
methods involve constructing agent-specific data and fine-tuning the models. On
the other hand, some methods focus on designing prompts that effectively
activate the reasoning abilities of the LLMs. We explore both strategies on the
7B and 13B models. We propose a comprehensive method for constructing
agent-specific data using GPT-4. Through supervised fine-tuning with
constructed data, we find that for these models with a relatively small number
of parameters, supervised fine-tuning can significantly reduce hallucination
outputs and formatting errors in agent tasks. Furthermore, techniques such as
multi-path reasoning and task decomposition can effectively decrease problem
complexity and enhance the performance of LLMs as agents. We evaluate our
method on five agent tasks of AgentBench and achieve satisfactory results.
