Hasnat Md Abdullah — accepted AI-conference papers

2026

DETONATE – A Benchmark for Text-to-Image Alignment and Kernelized Direct Preference Optimization

Renjith Prasad Kaippilly Mana, Abhilekh Borah, Hasnat Md Abdullah, Chathurangi Shyalika, Gurpreet Singh, Ritvik Garimella +6

AAAI 2026technical

Alignment is crucial for text-to-image (T2I) models to ensure that the generated images faithfully capture user intent while maintaining safety and fairness. Direct Preference Optimization (DPO) has emerged as a key alignment technique for large language models (LLMs), and its influence is now exten

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2025

Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations

Abhilekh Borah, Chhavi Sharma, Danush Khanna, Utkarsh Bhatt, Gurpreet Singh, Hasnat Md Abdullah +9

EMNLP 2025

Alignment is no longer a luxury; it is a necessity. As large language models (LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must reliably reflect human-aligned values and safety constraints. Yet current evaluations rely heavily on behavioral proxies

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2025

Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs

Kangda Wei, Hasnat Md Abdullah, Ruihong Huang

EMNLP 2025

Large Language Models (LLMs) often exhibit gender bias, resulting in unequal treatment of male and female subjects across different contexts. To address this issue, we propose a novel data generation framework that fosters exploratory thinking in LLMs. Our approach prompts models to generate story p

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