From text to traits: exploring the role of large language models in plant breeding
Bibliographic record
Abstract
Modern plant breeders regularly deal with the intricate patterns within biological data in order to better understand the biological background behind a trait of interest and speed up the breeding process. Recently, Large Language Models (LLMs) have gained widespread adoption in everyday contexts, showcasing remarkable capabilities in understanding and generating human-like text. By harnessing the capabilities of LLMs, foundational models can be repurposed to uncover intricate patterns within biological data, leading to the development of robust and flexible predictive tools that provide valuable insights into complex plant breeding systems. Despite the significant progress made in utilizing LLMs in various scientific domains, their adoption within plant breeding remains unexplored, presenting a significant opportunity for innovation. This review paper explores how LLMs, initially designed for natural language tasks, can be adapted to address specific challenges in plant breeding, such as identifying novel genetic interactions, predicting performance of a trait of interest, and well-integrating diverse datasets such as multi-omics, phenotypic, and environmental sources. Compared to conventional breeding methods, LLMs offer the potential to enhance the discovery of genetic relationships, improve trait prediction accuracy, and facilitate informed decision-making. This review aims to bridge this gap by highlighting current advancements, challenges, and future directions for integrating LLMs into plant breeding, ultimately contributing to sustainable agriculture and improved global food security.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".