arXiv:2606.04500v1 Announce Type: new Abstract: High-throughput microscopy generates large, structured datasets capturing cellular responses to pharmacological perturbations, but accessing these datasets typically requires SQL expertise. Large language models offer a natural-language alternative, yet their tendency to hallucinate raises concerns about result reliability . We present SANE Schema-Aware Natural-language Evaluation, a novel paradigm for domain-specific text-to-SQL evaluation: schema-grounded, automatically generated benchmarks tied to real and specific experimental structure. SANE
The proliferation of large language models and high-throughput biological data generation necessitates more reliable and accessible interfaces for scientific data analysis, addressing current limitations in NLP for structured queries.
A strategic reader should care because improving the reliability and interpretability of AI-driven biological data analysis can accelerate drug discovery, therapeutic development, and fundamental scientific understanding.
This research introduces a novel paradigm for evaluating text-to-SQL systems in specialized domains, potentially making complex biological datasets more accessible to researchers without needing SQL expertise, while mitigating AI hallucination risks.
- · Biological researchers
- · Pharmaceutical companies
- · Biotechnology sector
- · AI-driven life sciences platforms
- · Data analysts specialized in SQL for biology
- · Companies with less sophisticated NLP for scientific data
Biological scientists gain improved, more reliable natural language access to complex experimental datasets, accelerating hypothesis generation and validation.
The reduced barrier to data interaction could lead to an exponential increase in scientific discoveries derived from existing and future high-throughput biological experiments.
This could democratize access to advanced biological data analysis, enabling smaller labs and individual researchers to compete more effectively with larger institutions in areas like drug development.
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