Papers by Ahatsham Hayat
A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models (2025.coling-main)
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| Challenge: | Existing approaches to handle missing data in tabular datasets rely on numerical estimations instead of pre-trained language models (LMs). |
| Approach: | They propose a method that leverages pre-trained language models to create contextually relevant descriptors for missing values. |
| Outcome: | The proposed approach outperforms the best-performing baselines in MCAR, MAR, and MNAR scenarios and offers a cost-effective solution for resource-constrained environments. |
From Numbers to Narratives: Efficient Language Model-Based Detection for Safety-Critical Minority Classes (2026.findings-eacl)
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| Challenge: | Tabular datasets with high overall accuracy and poor performance on minority classes are often misaligned . a numbers to narratives framework improves overall accuracy by up to 22.43% in five of six datasets while maintaining computational feasibility. |
| Approach: | They propose a number-to-narrative framework that transforms tabular data into contextually rich descriptions. |
| Outcome: | The proposed framework achieves superior minority class F1-scores in five of six datasets. |
ConText-LE: Cross-Distribution Generalization for Longitudinal Experiential Data via Narrative-Based LLM Representations (2025.findings-emnlp)
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| Challenge: | Longitudinal experiential data offers rich insights into dynamic human states, yet building models that generalize across diverse contexts remains challenging. |
| Approach: | They propose a framework that investigates text representation strategies and output formulations to maximize large language model cross-distribution generalization for behavioral forecasting. |
| Outcome: | The proposed framework achieves 12.28% improvement in out-of-distribution accuracy and 11.99% improvement in F1 scores over binary classification methods. |