SubmissionNumber#=%=#197
FinalPaperTitle#=%=#BD-NLP at SemEval-2024 Task 2: Investigating Generative and Discriminative Models for Clinical Inference with Knowledge Augmentation
ShortPaperTitle#=%=#
NumberOfPages#=%=#7
CopyrightSigned#=%=#Shantanu Nath
JobTitle#==#
Organization#==#
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/statement is examined in this work. Our empirical study suggests that DeBERTa-lg, a discriminative general-purpose natural language inference model, obtains the highest F1 score of 0.77 on the test set, securing the fourth rank on the leaderboard. Intriguingly, the augmentation of knowledge yields subpar results across most cases.
Author{1}{Firstname}#=%=#Shantanu
Author{1}{Lastname}#=%=#Nath
Author{1}{Email}#=%=#shantanu.nath@studenti.unitn.it
Author{1}{Affiliation}#=%=#University of Trenro
Author{2}{Firstname}#=%=#Ahnaf Mozib
Author{2}{Lastname}#=%=#Samin
Author{2}{Username}#=%=#ahnaf_samin
Author{2}{Email}#=%=#ahnaf.samin@queensu.ca
Author{2}{Affiliation}#=%=#Queen's University

==========
èéáğö