Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare
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
Objectives: To guide language model (LM) selection by comparing finetuning vs. zero-shot use, generic pretraining vs. domain-adjacent vs. further domain-specific pretraining, and bidirectional language models (BiLMs) such as BERT vs. unidirectional LMs (LLMs) for clinical classification. Materials and Methods: We evaluated BiLMs (RoBERTa, PathologyBERT, Gatortron) and LLM (Mistral nemo instruct 12B) on three British Columbia Cancer Registry (BCCR) pathology classification tasks varying in difficulty/data size. We assessed zero-shot vs. finetuned BiLMs, zero-shot LLM, and further BCCR-specific pretraining using macro-average F1 scores. Results: Finetuned BiLMs outperformed zero-shot BiLMs and zero-shot LLM. The zero-shot LLM outperformed zero-shot BiLMs but was consistently outperformed by finetuned BiLMs. Domain-adjacent BiLMs generally outperformed generic BiLMs after finetuning. Further domain-specific pretraining boosted complex/low-data task performance, with otherwise modest gains. Conclusions: For specialized classification, finetuning BiLMs is crucial, often surpassing zero-shot LLMs. Domain-adjacent pretrained models are recommended. Further domain-specific pretraining provides significant performance boosts, especially for complex/low-data scenarios. BiLMs remain relevant, offering strong performance/resource balance for targeted clinical tasks.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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