mlsanityData to 7 Oct 2026

BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models

arXiv 2605.05758 ↗First posted 7 May 2026cs.CLcited by 0

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Despite the success of large language models (LLMs) on general-purpose tasks, their performance in highly specialized domains such as biomedicine remains unsatisfactory. A key limitation is the inability of LLMs to effectively leverage biomedical tools, which clinical experts and biomedical researchers rely on extensively in daily workflows. While recent general-domain tool-calling datasets have substantially improved the capabilities of LLM agents, existing efforts in the biomedical domain largely rely on in-context learning and restrict models to a small set of tools. To address this gap, we introduce BioTool, a comprehensive biomedical tool-calling dataset designed for fine-tuning LLMs. BioTool comprises 34 frequently used tools collected from the NCBI, Ensembl, and UniProt databases, along with 7,040 high-quality, human-verified query-API call pairs spanning variation, genomics, proteomics, evolution, and general biology. Fine-tuning a 4-billion-parameter LLM on BioTool yields substantial improvements in biomedical tool-calling performance, outperforming cutting-edge commercial LLMs such as GPT-5.1. Furthermore, human expert evaluations demonstrate that integrating a BioTool-fine-tuned tool caller significantly improves downstream answer quality compared to the same LLM without tool usage, highlighting the effectiveness of BioTool in enhancing the biomedical capabilities of LLMs. The full dataset and evaluation code are available at https://github.com/gxx27/BioTool

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 31 references that are in the map. First posted on arXiv 7 May 2026.

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9 of 31 references.

References · 9

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  3. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  4. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  5. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  6. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 609
  7. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 182
  8. 2023GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Informationcited by 115
  9. 2023Benchmarking large language models for biomedical natural language processing applications and recommendationscited by 78

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  5. 2024Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
  6. 2025BioMedGPT-Mol: Multi-task Learning for Molecular Understanding and Generation
  7. 2026BioEVAL: A global, multi-institutional benchmark of large language and multimodal models for bioengineering
  8. 2024BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine