Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act. However, most of these agents rely on few-shot prompting techniques with off-the-shelf LMs. In this paper, we investigate and argue for the overlooked direction of fine-tuning LMs to obtain language agents. Using a setup of question answering (QA) with a Google search API, we explore a variety of base LMs, prompting methods, fine-tuning data, and QA tasks, and find language agents are consistently improved after fine-tuning their backbone LMs. For example, fine-tuning Llama2-7B with 500 agent trajectories generated by GPT-4 leads to a 77% HotpotQA performance increase. Furthermore, we propose FireAct, a novel approach to fine-tuning LMs with trajectories from multiple tasks and prompting methods, and show having more diverse fine-tuning data can further improve agents. Along with other findings regarding scaling effects, robustness, generalization, efficiency and cost, our work establishes comprehensive benefits of fine-tuning LMs for agents, and provides an initial set of experimental designs, insights, as well as open questions toward language agent fine-tuning.
The paper advocates for fine-tuning language models (LMs) to create more robust and efficient language agents, showing significant improvements when using smaller LMs.
Empirical evaluations using models like Llama-2, CodeLlama, and GPT-3.5 on QA tasks demonstrate enhanced performance, reduced inference times, and better generalization.
The methodology includes generating fine-tuning data using advanced LMs, incorporating diverse prompting methods, and systematically evaluating outcomes to validate the approach.
The paper "Toward Language Agent Fine-tuning" addresses an important and often overlooked area of research: the fine-tuning of language models (LMs) to function as language agents. Language agents, which leverage LMs to interact with external environments and perform sequential reasoning and actions, offer promising applications but currently suffer from limitations due to their typical reliance on few-shot prompting techniques.
The primary contributions of this paper can be summarized as follows:
The empirical results obtained are compelling:
The methodology of this paper stands out in its systematic approach:
The implications of this research are broad and significant:
Speculating on future developments, it is likely that incorporating more complex and dynamic models of environment interaction will be an area of growth. Furthermore, advancements in parameter-efficient fine-tuning methods could make the process even more accessible. Another avenue for exploration is to extend this fine-tuning approach to more diverse and complex tasks beyond QA, such as interactive dialogue systems or multi-agent environments.
This paper provides a rigorous and comprehensive study on the benefits of fine-tuning LMs for language agent applications. It lays down a strong foundation for future work in this area and provides practical insights and methodologies that can be adapted for various AI applications. The results show clear, quantifiable improvements in performance, efficiency, robustness, and generalization, making a strong case for the adoption of fine-tuning practices in the development of language agents.