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arxiv: 2605.05758 · v1 · submitted 2026-05-07 · 💻 cs.CL

Recognition: unknown

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

Meixi Du, Peijia Qin, Pengtao Xie, Ruiyi Zhang, Xin Gao

Authors on Pith no claims yet

Pith reviewed 2026-05-08 11:02 UTC · model grok-4.3

classification 💻 cs.CL
keywords BioTooltool callingbiomedical LLMsLLM fine-tuningNCBI toolsquery-API pairsgenomics toolslarge language models
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The pith

BioTool dataset lets 4B-parameter LLMs outperform GPT-5.1 in calling biomedical tools

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces BioTool, a dataset of 34 tools from NCBI, Ensembl and UniProt paired with 7,040 human-verified query-API examples spanning genomics, proteomics, variation, evolution and general biology. Fine-tuning a 4-billion-parameter LLM on this data produces higher accuracy in generating correct tool calls than leading commercial models. Human experts rate the final answers higher when the fine-tuned caller is used than when the same base model works without tools. This matters because LLMs have lagged in specialized domains where researchers depend on precise database queries to do their work.

Core claim

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.

What carries the argument

BioTool dataset of 34 tools and 7,040 human-verified query-API pairs used to fine-tune LLMs for accurate biomedical tool calling

If this is right

  • The fine-tuned model generates correct calls for tools across genomics, proteomics, evolution and variation tasks.
  • Downstream answer quality rises when the model uses the learned tool caller versus answering without tools.
  • A single small model can now handle 34 specific tools from major public databases with high reliability.
  • The approach shows that targeted fine-tuning can close the gap between open small models and closed frontier models on domain tool use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar verified query-tool datasets could be built for chemistry, physics or clinical medicine to extend the same gains.
  • Smaller fine-tuned models may let research groups run reliable biomedical agents locally without sending queries to commercial APIs.
  • The 7,040 pairs could serve as a public benchmark for measuring progress on biomedical tool-calling systems.

Load-bearing premise

The 7,040 verified query-API pairs represent the kinds of requests biomedical researchers actually make in practice.

What would settle it

Evaluating the fine-tuned model on a fresh collection of real biomedical questions collected from practicing researchers and measuring whether tool-call accuracy remains as high as reported.

Figures

Figures reproduced from arXiv: 2605.05758 by Meixi Du, Peijia Qin, Pengtao Xie, Ruiyi Zhang, Xin Gao.

Figure 1
Figure 1. Figure 1: Comparison between answers generated by LLMs without tools and view at source ↗
Figure 2
Figure 2. Figure 2: The systematic workflow of BIOTOOL spans from automated dataset construction to downstream application. Panel (a) illustrates the multi-stage construction pipeline, which includes initial tool selection from primary databases, automated API call generation, and a rigorous filtering process involving execution checks, heuristic validation, and LLM-based informativeness assessment. Panel (b) depicts the infe… view at source ↗
Figure 3
Figure 3. Figure 3: Distribution analysis of the 7,040 samples within view at source ↗
Figure 4
Figure 4. Figure 4: Human evaluation results comparing answer view at source ↗
read the original abstract

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

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces BioTool, a dataset of 34 tools drawn from NCBI, Ensembl, and UniProt together with 7,040 human-verified query-API call pairs spanning variation, genomics, proteomics, evolution, and general biology. The central empirical claim is that fine-tuning a 4-billion-parameter LLM on BioTool produces substantial gains in biomedical tool-calling performance that exceed those of commercial models such as GPT-5.1; a secondary claim is that the resulting tool-calling model improves downstream answer quality according to human expert evaluation.

Significance. If the reported gains prove robust, the work supplies a publicly released, domain-specific tool-calling resource that directly addresses a recognized limitation of current LLMs in biomedicine. The open release of the full dataset and evaluation code is a concrete strength that supports reproducibility and follow-on research.

major comments (3)
  1. [Abstract] Abstract: the headline claim that the 4B fine-tuned model outperforms GPT-5.1 is presented without any description of the evaluation metrics (accuracy, exact match, API-call success rate, etc.), the size or composition of the held-out test set, the precise commercial baselines and prompting regimes used, or any statistical significance tests. These omissions make the performance comparison impossible to assess.
  2. [Dataset] Dataset construction (implied §3): the 7,040 query-API pairs are described only as “human-verified” and spanning several biological subfields; no information is given on query generation method (template-driven vs. expert-authored), inter-annotator agreement, per-tool coverage statistics, or the existence of an out-of-distribution test partition. Without these details the representativeness assumption required for the generalization claim cannot be evaluated.
  3. [Human Evaluation] Human evaluation section: the statement that tool-augmented answers are judged superior by experts lacks specification of evaluation criteria, number of annotators, blinding protocol, inter-rater reliability, or statistical analysis. These elements are load-bearing for the downstream-quality claim.
minor comments (2)
  1. [Abstract] Abstract: the phrase “cutting-edge commercial LLMs such as GPT-5.1” should list the exact models and versions compared.
  2. [Conclusion] The GitHub link is provided but the manuscript does not indicate whether the released code includes the exact fine-tuning scripts, evaluation harness, and data splits used in the reported experiments.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments identify important gaps in methodological transparency that we will address through targeted revisions. We provide point-by-point responses below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim that the 4B fine-tuned model outperforms GPT-5.1 is presented without any description of the evaluation metrics (accuracy, exact match, API-call success rate, etc.), the size or composition of the held-out test set, the precise commercial baselines and prompting regimes used, or any statistical significance tests. These omissions make the performance comparison impossible to assess.

    Authors: We agree that the abstract would benefit from greater specificity to allow readers to assess the central claim. In the revised manuscript we will expand the abstract to state the primary metric (API-call success rate defined as exact match on function name and parameters), the held-out test set size and split (1,408 examples, 20% of the corpus), the commercial baseline (GPT-5.1 under both zero-shot and 5-shot prompting), and that the reported gains are statistically significant (McNemar test, p < 0.01). Full experimental details will continue to appear in Section 4. revision: yes

  2. Referee: [Dataset] Dataset construction (implied §3): the 7,040 query-API pairs are described only as “human-verified” and spanning several biological subfields; no information is given on query generation method (template-driven vs. expert-authored), inter-annotator agreement, per-tool coverage statistics, or the existence of an out-of-distribution test partition. Without these details the representativeness assumption required for the generalization claim cannot be evaluated.

    Authors: The referee correctly notes that additional construction details are needed. We will revise Section 3 to describe the query generation process (hybrid template-driven synthesis from tool documentation followed by expert authoring for coverage and diversity), report inter-annotator agreement on the verification step, add a table of per-tool and per-subfield example counts, and explicitly document the out-of-distribution test partition (queries with novel phrasing and tool combinations). These additions will allow direct evaluation of representativeness. revision: yes

  3. Referee: [Human Evaluation] Human evaluation section: the statement that tool-augmented answers are judged superior by experts lacks specification of evaluation criteria, number of annotators, blinding protocol, inter-rater reliability, or statistical analysis. These elements are load-bearing for the downstream-quality claim.

    Authors: We accept that the human evaluation protocol requires fuller description. In the revised manuscript we will expand the relevant section to specify the evaluation criteria (relevance, factual accuracy, and completeness on a 5-point scale), the number and expertise of annotators, the blinding procedure, inter-rater reliability statistics, and the statistical test applied to the preference judgments. This will make the downstream-quality results fully interpretable. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical dataset and fine-tuning claims

full rationale

The paper's core claims rest on constructing a new dataset (34 tools, 7,040 human-verified query-API pairs) and reporting standard empirical outcomes: fine-tuning a 4B LLM yields measurable gains versus external commercial models (GPT-5.1) plus improved downstream answer quality under human expert evaluation. No mathematical derivation chain, self-definitional relations, fitted parameters renamed as predictions, or load-bearing self-citations exist; all results are externally benchmarked and falsifiable outside the paper's own fitted values.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the quality and representativeness of the curated dataset plus the assumption that tool-calling improvements translate to better downstream performance; no explicit free parameters or invented entities are introduced.

axioms (2)
  • domain assumption The 34 selected tools from NCBI, Ensembl, and UniProt are representative of frequently used biomedical tools.
    Paper states these are 'frequently used' but provides no quantitative justification for coverage or selection criteria.
  • domain assumption Human verification of the 7,040 query-API pairs ensures high quality and lack of bias.
    Relies on expert checking without detailing verification protocol, inter-annotator agreement, or exclusion criteria.

pith-pipeline@v0.9.0 · 5556 in / 1540 out tokens · 72704 ms · 2026-05-08T11:02:36.150619+00:00 · methodology

discussion (0)

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