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HFEPX · Eval paper review

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

Mathew J. Koretsky, Maya Willey, Owen Bianchi, Chelsea X. Alvarado +6 more

Published

May 23, 2025

Citations

0

Trust level

Moderate

Usefulness score

25/100 (Low)

Extraction confidence

55% (Moderate)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Aug 12, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Automatic Metrics Long Horizon Medicine Multilingual

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.

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

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

What to verify

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

Main weakness

No major weakness surfaced.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
25/100
Adjacent candidate

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

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

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.

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%."

Benchmarks and datasets

Biomedbench

Reported metrics

accuracy
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

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.

Recommended queries

Expert verification evaluation Accuracy

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

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: Biomedbench

  • Metric reporting is present

    Detected: accuracy