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BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases

Mathew J. Koretsky, Maya Willey, Owen Bianchi, Chelsea X. Alvarado, Tanay Nayak, Nicole Kuznetsov, Sungwon Kim, Mike A. Nalls, Daniel Khashabi, Faraz Faghri · May 23, 2025 · Citations: 0

Automatic Metrics Long Horizon Medicine Multilingual
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How to use this page

Moderate trust

Use this for comparison and orientation, not as your only source.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Moderate

Derived from extracted protocol signals and abstract evidence.

Abstract

Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base. BiomedSQL comprises 68,000 question/SQL query/answer triples generated from templates and grounded in a harmonized BigQuery database that integrates gene-disease associations, causal inference from omics data, and drug approval records. Each question requires models to infer domain-specific criteria, such as genome-wide significance thresholds, effect directionality, or trial phase filtering, rather than rely on syntactic translation alone. We evaluate a range of open- and closed-source LLMs across prompting strategies and interaction paradigms. Our results reveal a substantial performance gap: Gemini-3-Pro achieves 58.1% execution accuracy under baseline prompting, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%. BiomedSQL provides a new foundation for advancing text-to-SQL systems that support scientific discovery through robust reasoning over structured biomedical knowledge bases. The BiomedSQL benchmark and codebase are publicly available at https://datatecnica.github.io/biomedbench-suite/biomedsql.

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

Main weakness

No major weakness surfaced.

Trust level

Moderate

Usefulness score

25/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 55%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks."

Benchmarks / Datasets

strong

Biomedbench

Useful for quick benchmark comparison.

"The BiomedSQL benchmark and codebase are publicly available at https://datatecnica.github.io/biomedbench-suite/biomedsql."

Reported Metrics

strong

Accuracy

Useful for evaluation criteria comparison.

"Our results reveal a substantial performance gap: Gemini-3-Pro achieves 58.1% execution accuracy under baseline prompting, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%."

Rater Population

strong

Domain Experts

Helpful for staffing comparability.

"Our results reveal a substantial performance gap: Gemini-3-Pro achieves 58.1% execution accuracy under baseline prompting, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: Medicine, Multilingual

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: Long Horizon
  • Quality controls: Not reported
  • Evidence quality: Moderate
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

Biomedbench

Reported Metrics

accuracy

Research Brief

Metadata summary

Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks.
  • However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required.
  • We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics, Long-horizon tasks) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base.
  • We evaluate a range of open- and closed-source LLMs across prompting strategies and interaction paradigms.
  • Our results reveal a substantial performance gap: Gemini-3-Pro achieves 58.1% execution accuracy under baseline prompting, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%.

Why It Matters For Eval

  • We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base.
  • Our results reveal a substantial performance gap: Gemini-3-Pro achieves 58.1% execution accuracy under baseline prompting, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: Biomedbench

  • Pass: Metric reporting is present

    Detected: accuracy

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