Natural Language Querying of Biological Databases with Large Language Models
Bibliographic record
Abstract
It is attractive to be able to query biological knowledge bases using queries written in a natural language. Natural language queries are typically translated into structured queries using Large Language Models (LLM). However, unassisted naive LLMs often fail to interpret the scientific queries correctly and also may produce linguistically correct but factually false output colloquially known as hallucinations. Here we report the outcomes of a systematic evaluation of several techniques aimed at querying biological databases in natural language with LLMs. We tested multiple LLMs, both proprietary and open-source, on a knowledge graph representing a subset of the Open Targets database frequently used in drug discovery. We find that the best balance between accuracy and flexibility in this context is achieved by multiple LLM agents that can challenge the outputs of each other and interact with a human user. This strategy is very flexible and does not require prior knowledge of the data structure, query templates, secondary databases, or adaptor language models, and it exceeds the query accuracy of the other techniques. We also highlight the need for appropriate benchmarks for testing the ability to query large biological knowledge bases with LLMs, and propose future directions for this work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".