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Ahatsham Hayat

2 accepted papers

2025

A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models

COLING 2025main

This paper presents a novel approach named Contextually Relevant Imputation leveraging pre-trained Language Models (CRILM) for handling missing data in tabular datasets. Instead of relying on traditional numerical estimations, CRILM uses pre-trained language models (LMs) to create contextually relev…

Cited by 1SourcePDFScholar
2025

ConText-LE: Cross-Distribution Generalization for Longitudinal Experiential Data via Narrative-Based LLM Representations

EMNLP 2025

Longitudinal experiential data offers rich insights into dynamic human states, yet building models that generalize across diverse contexts remains challenging. We propose ConText-LE, a framework that systematically investigates text representation strategies and output formulations to maximize large

Cited by 0SourcePDFScholar