BD-NLP at SemEval-2024 Task 2: Investigating Generative and Discriminative Models for Clinical Inference with Knowledge Augmentation
Bibliographic record
Abstract
Healthcare professionals rely on evidence from Clinical Trial Records (CTRs) to devise treatment plans.However, the increasing quantity of CTRs poses challenges in efficiently assimilating the latest evidence to provide personalized evidence-based care.In this paper, we present our solution to the SemEval-2024 Task 2 titled "Safe Biomedical Natural Language Inference for Clinical Trials".Given a statement and one/two CTRs as inputs, the task is to determine whether or not the statement entails or contradicts the CTRs.We explore both generative and discriminative large language models (LLM) to investigate their performance for clinical inference.Moreover, we contrast the general-purpose LLMs with the ones specifically tailored for the clinical domain to study the potential advantage in mitigating distributional shifts.Furthermore, the benefit of augmenting additional knowledge within the prompt is examined in this work.Our empirical study suggests that DeBERTa-lg, a discriminative task-specific natural language inference model, obtains the highest F1 score of 0.77 and consistency score of 0.76 on the test set, securing the fourth rank on the leaderboard.Intriguingly, the augmentation of knowledge yields subpar results across most cases.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".