MediTriR: A Triple-Driven Approach to Retrieval-Augmented Generation for Medical Question Answering Tasks
Bibliographic record
Abstract
Advances in large language models have driven progress in medical question-answering systems, but challenges remain in accuracy and relevance, especially in complex medical settings. To address this problem, we introduce MediTriR. This approach combines knowledge graph triples with a retrieval-augmented generation framework to enhance the performance of large language models in medical multiple-choice question-answering tasks. Our approach transforms the retrieved medical knowledge into structured triples and uses these triples for accurate information retrieval. Furthermore, we dynamically generate new triples based on the properties of triples to enrich the model's reasoning ability. The integration of retrieval-augmented generation enables more accurate and contextual answers by leveraging external medical triples. MediTriR is evaluated against pure large language model approaches using the MedMCQA and Medical QA datasets, showing better performance in terms of accuracy and relevance. These results highlight the potential of our approach to improve medical decision support and education.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".