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From the 1 of 20 linked papers with an AI index.

collaborators

20 papers

cs.IR2026

Can Argus Judge Them All? Comparing VLMs Across Domains

Harsh Joshi, Gautam Siddharth Kashyap, Rafiq Ali +5

The paper introduces ARGUS-EVAL, a framework that assesses vision-language models on both capability and reliability across domains, and uses it to compare several VLMs on retrieva…

cs.CL2026

ChildGuard: A Specialized Dataset for Combatting Child-Targeted Hate Speech

Gautam Siddharth Kashyap, Mohammad Anas Azeez, Rafiq Ali +3

Mental health industry faces growing concerns regarding hate speech directed at children's on social media, as exposure to such content can contribute to adverse psychological outc…

cs.CL2026

Truth, Trust, and Trouble: Medical AI on the Edge

Mohammad Anas Azeez, Rafiq Ali, Ebad Shabbir +4

Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering. However, ensuring these models meet critical…

cs.CL2026

We Think, Therefore We Align LLMs to Helpful, Harmless and Honest Before They Go Wrong

Gautam Siddharth Kashyap, Mark Dras, Usman Naseem

Alignment of Large Language Models (LLMs) is the ability to satisfy desired objectives during generation, which is critical for trustworthy deployment. In practice, alignment is of…

cs.CL2026

Are Large Language Models Economically Viable for Industry Deployment?

Abdullah Mohammad, Sushant Kumar Ray, Pushkar Arora +5

Generative AI-powered by Large Language Models (LLMs)-is increasingly deployed in industry across healthcare decision support, financial analytics, enterprise retrieval, and conver…

cs.CL2026

AlignCultura: Towards Culturally Aligned Large Language Models?

Gautam Siddharth Kashyap, Mark Dras, Usman Naseem

Cultural alignment in Large Language Models (LLMs) is essential for producing contextually aware, respectful, and trustworthy outputs. Without it, models risk generating stereotype…