Enhancing Robustness in Biomedical NLI Models: A Probing Approach for
  Clinical Trials
Ata Mustafa
Large Language Models have revolutionized various fields and industries, such
as Conversational AI, Content Generation, Information Retrieval, Business
Intelligence, and Medical, to name a few. One major application in the field of
medical is to analyze and investigate clinical trials for entailment
tasks.However, It has been observed that Large Language Models are susceptible
to shortcut learning, factual inconsistency, and performance degradation with
little variation in context. Adversarial and robust testing is performed to
ensure the integrity of models output. But, ambiguity still persists. In order
to ensure the integrity of the reasoning performed and investigate the model
has correct syntactic and semantic understanding probing is used. Here, I used
mnestic probing to investigate the Sci-five model, trained on clinical trial. I
investigated the model for feature learnt with respect to natural logic. To
achieve the target, I trained task specific probes. Used these probes to
investigate the final layers of trained model. Then, fine tuned the trained
model using iterative null projection. The results shows that model accuracy
improved. During experimentation, I observed that size of the probe has affect
on the fine tuning process.