Search Results for author:
Found 9 papers, 8 papers with code
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations
Additionally, we facilitated future research and development by releasing a Python module for medical LLM evaluation and establishing a dedicated leaderboard on Hugging Face for medical domain LLMs.
CLIFT: Analysing Natural Distribution Shift on Question Answering Models in Clinical Domain
Our findings emphasize the need for and the potential for increasing the robustness of clinical domain models under distributional shifts.
Med-HALT: Medical Domain Hallucination Test for Large Language Models
This research paper focuses on the challenges posed by hallucinations in large language models (LLMs), particularly in the context of the medical domain.
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges
Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage.
DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction
This paper introduces DeepParliament, a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks.
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.
Ranked #7 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing
COVID-19 (coronavirus disease 2019) pandemic caused by SARS-CoV-2 has led to a treacherous and devastating catastrophe for humanity.
Multi-Label Text Classification using Attention-based Graph Neural Network
The graph attention network uses a feature matrix and a correlation matrix to capture and explore the crucial dependencies between the labels and generate classifiers for the task.
MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network
The graph attention network uses a feature matrix and a correlation matrix to capture and explore the crucial dependencies between the labels and generate classifiers for the task.
Ranked #1 on
Multi-Label Text Classification
on Slashdot
