CLAIM Your Data: Enhancing Imputation Accuracy with Contextual Large Language Models

no code implementations • 28 May 2024 • Ahatsham Hayat, Mohammad Rashedul Hasan

This paper introduces the Contextual Language model for Accurate Imputation Method (CLAIM), a novel strategy that capitalizes on the expansive knowledge and reasoning capabilities of pre-trained large language models (LLMs) to address missing data challenges in tabular datasets.

A Study of the Generalizability of Self-Supervised Representations

no code implementations • 19 Sep 2021 • Atharva Tendle, Mohammad Rashedul Hasan

We show that the SSL representations are more generalizable as compared to the SL representations.

Self-Supervised Learning Transfer Learning

Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning

no code implementations • EMNLP 2021 • Yuanzhi Chen, Mohammad Rashedul Hasan

Irrespective of the success of the deep learning-based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from COVID-19 social media data.

Misinformation Transfer Learning