3 papers
cs.CL2026
The Abstraction Gap in Vision-Language Causal Reasoning
Chinh Hoang, Mohammad Rashedul Hasan
Vision-language models (VLMs) generate fluent causal explanations, but current evaluations cannot distinguish linguistic plausibility from faithful causal reasoning. We introduce a…
cs.LG2025
Leveraging Language Models for Analyzing Longitudinal Experiential Data in Education
Ahatsham Hayat, Bilal Khan, Mohammad Rashedul Hasan
We propose a novel approach to leveraging pre-trained language models (LMs) for early forecasting of academic trajectories in STEM students using high-dimensional longitudinal expe…
cs.CL2025
A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models
Ahatsham Hayat, Mohammad Rashedul Hasan
This paper presents a novel approach named \textbf{C}ontextually \textbf{R}elevant \textbf{I}mputation leveraging pre-trained \textbf{L}anguage \textbf{M}odels (\textbf{CRILM}) for…