Large Language Models for Multi-Choice Question Classification of Medical Subjects
The aim of this paper is to evaluate whether large language models trained on multi-choice question data can be used to discriminate between medical subjects. This is an important and challenging task for automatic question answering. To achieve this goal, we train deep neural networks for multi-class classification of questions into the inferred medical subjects. Using our Multi-Question (MQ) Sequence-BERT method, we outperform the state-of-the-art results on the MedMCQA dataset with an accuracy of 0.68 and 0.60 on their development and test sets, respectively. In this sense, we show the capability of AI and LLMs in particular for multi-classification tasks in the Healthcare domain.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-class ClassificationQuestion AnsweringSimilar Papers 제목 키워드 기반
You May Speak Freely: Improving the Fine-Grained Visual Recognition Capabilities of Multimodal Large Language Models with Answer Extraction
Despite the renewed interest in zero-shot visual classification due to the rise of Multimodal Large Language Models (MLLMs), the problem of evaluating free-form responses of auto-regressive models remains a persistent ch…
Fine-Grained Visual RecognitionJU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System
This paper describes the participation of the JU NITM team in IJCNLP-2017 Task 5: {``}Multi-choice Question Answering in Examinations{''}. The main aim of this shared task is to choose the correct option for each multi-c…
Answer SelectionClassificationCommunity Question AnsweringGeneral Classification+5Conformal Prediction with Large Language Models for Multi-Choice Question Answering
As large language models continue to be widely developed, robust uncertainty quantification techniques will become crucial for their safe deployment in high-stakes scenarios. In this work, we explore how conformal predic…
Conformal PredictionMultiple-choicePredictionQuestion Answering+1SailCompass: Towards Reproducible and Robust Evaluation for Southeast Asian Languages
In this paper, we introduce SailCompass, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA). SailCompass encompasses three main SEA languages, eig…
Multiple-choiceTransfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question. The existing methods employ the pre-trained language model as the encoder, …
Binary ClassificationLanguage ModelingLanguage ModellingMachine Reading Comprehension+4