Large Language Models for Biological Knowledge Extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it