PET ool LLM : Towards Personalized Tool Learning in Large Language Models (2025)

Abstract: Tool learning has emerged as a promising direction by extending Large Language Models’ (LLMs) capabilities with external tools. Existing tool learning studies primarily focus on the general-purpose tool-use capability, which addresses explicit user requirements in instructions. However, they overlook the importance of personalized tool-use capability, leading to an inability to handle implicit user preferences. To address the limitation, we first formulate the task of personalized tool learning, which integrates user’s interaction history towards personalized tool usage. To fill the gap of missing benchmarks, we construct PEToolBench, featuring diverse user preferences reflected in interaction history under three distinct personalized settings, and encompassing a wide range of tool-use scenarios. Moreover, we propose a framework PEToolLLaMA to adapt LLMs to the personalized tool learning task, which is trained through supervised fine-tuning and direct preference optimization. Extensive experiments on PEToolBench demonstrate the superiority of PEToolLLaMA over existing LLMs. We release code and data at https://github.com/travis-xu/PEToolBench.

## introduction large language models (llms) possess extensive knowledge and powerful instruction following abilities, making them effective ai assistants for tasks such as text rewriting, question answering, and code writing @xcite . however, they often struggle to address user needs in scenarios such as checking weather and booking flights. to address this, tool learning @xcite has emerged as a promising solution by allowing llms to use external tools, such as real-time weather apis and booking systems. in this way, tool learning has extended llms' capabil- † corresponding author. current tool learning procedure typically begins with a user instruction, and then llms are required to use tools with appropriate functionalities for satisfying users' needs. existing tool learning methods can be categorized into in-context learning @xcite and finetuning approaches @xcite . the former approach allows llms to use tools by directly providing tool documentation in input but the performance is constrained by the input length. the latter approach trains llms to internalize tool knowledge but struggles with tool generalization. despite the advancement, existing tool learning methods primarily focus on the general-purpose tool-use capability but overlook the critical role of personalization. in tool learning, more personalized user needs are expected to be derived from the user's previous tool usage history as a supplement to user instructions, which can help llms provide more customized tool-usage assistance to enhance the user experience. as illustrated in figure 1 , personalized tool learning is non-trivial due to the following aspects. 1) implicit user preferences. user preferences for tool usage are often implicitly conveyed through the user's history rather than explicitly stated in user instructions, making them difficult to understand. for instance, when a user requests a search for articles, their preference for academic-related content needs to be inferred from previous interactions with academic tools like google scholar. 2) non-functional tool attributes. since many tools have the same functionalities, user preferences cannot be effectively distinguished based solely on tool functionalities. this underscores the need to consider non-functional tool attributes, such as usability, integrability, and accessibility, which can better reflect user preferences. as shown in figure 1 , google search can be i want to search for articles related to deepseek. ## conclusion and future work in this paper, we advanced general-purpose tooluse llms into personalized tool-use llms, aiming to provide users with customized tool-usage assistance. we formulate the task of personalized tool learning and identify the goal of leveraging user's interaction history to achieve implicit preference understanding and personalized tool calling. for training and evaluation, we construct the first petoolbench benchmark, featuring diverse users' interaction history in three types. we also propose a novel personalized framework petoolllama conducted under a two-stage training process to endow llms with personalized tool-use capabilities. extensive experiments on petoolbench demonstrate that petoolllama consistently surpasses existing baselines, effectively meeting user requirements and preferences. we believe that the task, benchmark, and framework for personalized tool learning will broaden the research scope, introduce new challenges and inspire novel methods. in the future, we aim to enhance this work from the following dimensions. 1) we plan to explore more heterogeneous personal user data beyond interaction history, such as user profiles or personas. this will allow us to reflect user preferences from multiple dimensions, providing a more comprehensive evaluation on the personalized tool-use capabilities of llms. 2) currently, our work is limited to tool-usage scenarios involving a single tool. in the future, we intend to expand to more complex personalized tool-usage, such as multi-tool scenarios. these scenarios will require llms to perform personalized tool planning and engage in multiround tool calling to address user needs effectively. limitations 1) due to the lack of real user interaction histories on tool usage, we utilize llm to synthesize such data. however, this approach may compromise the authenticity and reliability of the data, which is a common challenge in data synthesis methods. to mitigate this issue, we incorporate pre-constructed user preference information into the data generation process. this strategy helps guide llm in generating contextually relevant outputs, thereby improving the quality and consistency of the synthesized data. 2) in real-world scenarios, tools have multiple dimensions of attributes. however, due to the limited information contained in current tool documentation, it is difficult to fully identify and exploit all possible tool attributes. fortunately, the attributes we have obtained are sufficient to differentiate between tools, enabling us to effectively construct user preferences.