Papers by Abdullah Mohammad

2 papers
Are Large Language Models Economically Viable for Industry Deployment? (2026.acl-industry)

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Challenge: Generative AI is increasingly deployed in healthcare, financial analytics, and conversational automation.
Approach: They propose a framework that evaluates large language models across their full lifecycle on legacy GPUs.
Outcome: The proposed framework evaluates LLMs across their full lifecycle on legacy GPUs.
Do Large Language Models Reflect Demographic Pluralism in Safety? (2026.findings-eacl)

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Challenge: Existing datasets that focus on demographics and safety are narrow in their annotator pools.
Approach: They propose to decouple value framing from responses by modeling pluralism directly at the prompt level.
Outcome: Demo-SafetyBench decouples value framing from responses to model pluralism at the prompt level.

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