Large Language Models for Biological Knowledge Extraction
Bibliographic record
Abstract
The surge in biomedical literature has led to severe information overload for researchers, necessitating automated knowledge extraction tools. Large Language Models (LLMs), which have emerged in recent years, demonstrate superior performance in text understanding and generation, providing a new approach for biological knowledge extraction. This study reviews the applications of LLMs in tasks such as named entity recognition, relation extraction, and event extraction, and discusses their latest advancements in subfields such as genomics, proteomics, and pharmacology. The advantages of LLMs over traditional methods in contextual understanding and semantic representation are analyzed, along with the optimization effects of domain adaptation, fine-tuning, and cue engineering on model performance. A case study of extracting gene-disease associations using the BioGPT model demonstrates the application process and effectiveness of LLMs, while also analyzing challenges related to data quality, model illusion, and privacy protection. The future directions of LLM integration with knowledge graphs, multimodal data integration, and knowledge verification are discussed, along with related ethical considerations. These advancements are expected to provide new paradigms for future biomedical research.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".