@inproceedings{sengupta-etal-2026-tooldreamer,
    title = "{T}ool{D}reamer: Instilling {LLM} Reasoning Into Tool Retrievers",
    author = "Sengupta, Saptarshi  and
      Zhou, Zhengyu  and
      Araki, Jun  and
      Wang, Xingbo  and
      Wang, Bingqing  and
      Wang, Suhang  and
      Feng, Zhe",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.254/",
    pages = "5465--5482",
    ISBN = "979-8-89176-380-7",
    abstract = "Tool calling has become increasingly popular for Large Language Models (LLMs). However, for large tool sets, the resulting tokens wouldexceed the LLM{'}s context window limit, making it impossible to include every tool. Hence, an external retriever is used to provide LLMswith the most relevant tools for a query. Existing retrieval models rank tools based on the similarity between a user query and a tool description (TD). This leads to suboptimal retrieval as user requests are often poorly aligned with the language of TD. To remedy the issue, we propose ToolDreamer, a framework that conditions retriever models to fetch tools based on hypothetical (synthetic) TD generated using an LLM, i.e., descriptions of tools that the LLM feels will be potentially useful for the query. The framework enables a more natural alignment between queries and tools within the language space of TD{'}s. We apply ToolDreamer on the ToolRet dataset and show that our method improves the performance of sparse and dense retrievers with and without training, showcasing its flexibility. With our proposed framework, we aim to offload a portion of the reasoning burden to the retriever so that the LLM may effectively handle a large collection of tools without inundating its context window."
}