Taxonomy Portraits: Deciphering the Hierarchical Relationships of Medical Large Language Models
Bibliographic record
Abstract
Background: Large language models (LLMs) continue to enjoy enterprise-wide adoption in health care while evolving in number, size, complexity, cost, and most importantly performance. Performance benchmarks play a critical role in their ranking across community leaderboards and subsequent adoption. Objective: Given the small operating margins of health care organizations and growing interest in LLMs and conversational artificial intelligence (AI), there is an urgent need for objective approaches that can assist in identifying viable LLMs without compromising their performance. The objective of the present study is to generate taxonomy portraits of medical LLMs (n=33) whose domain-specific and domain non-specific multivariate performance benchmarks were available from Open-Medical LLM and Open LLM leaderboards on Hugging Face. Methods: Hierarchical clustering of multivariate performance benchmarks is used to generate taxonomy portraits revealing inherent partitioning of the medical LLMs across diverse tasks. While domain-specific taxonomy is generated using nine performance benchmarks related to medicine from the Hugging Face Open-Medical LLM initiative, domain non-specific taxonomy is presented in tandem to assess their performance on a set of six benchmarks and generic tasks from the Hugging Face Open LLM initiative. Subsequently, non-parametric Wilcoxon rank-sum test and linear correlation are used to assess differential changes in the performance benchmarks between two broad groups of LLMs and potential redundancies between the benchmarks. Results: Two broad families of LLMs with statistically significant differences (α=.05) in performance benchmarks are identified for each of the taxonomies. Consensus in their performance on the domain-specific and domain non-specific tasks revealed robustness of these LLMs across diverse tasks. Subsequently, statistically significant correlations between performance benchmarks revealed redundancies, indicating that a subset of these benchmarks may be sufficient in assessing the domain-specific performance of medical LLMs. Conclusions: Understanding medical LLM taxonomies is an important step in identifying LLMs with similar performance while aligning with the needs, economics, and other demands of health care organizations. While the focus of the present study is on a subset of medical LLMs from the Hugging Face initiative, enhanced transparency of performance benchmarks and economics across a larger family of medical LLMs is needed to generate more comprehensive taxonomy portraits for accelerating their strategic and equitable adoption in health care.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".