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Author

Anubhav Gupta

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#natural language process... Preprint Aug 2026

When Who You Are Can Change the Code You Get: A Study of Persona-Induced Bias in LLM Code Generation

Large Language Models (LLMs) are widely used as programming assistants, yet it remains unclear whether and how user's demographic information impacts the technical quality of generated code. We conduct a large-scale empirical study of persona-induced bias in LLM-based code generation, focusing a proprietary model (Gemi...

Anubhav Gupta, M. Figueiredo, L. Machado et al. · 0 citations
Open access Jul 2026

Do influence tactics matter? investigating prompt framing effects in LLM code generation

This work presents the first large-scale empirical study of influence-induced prompt framing in software engineering tasks, offering insights into how linguistic cues may shape LLM outputs and practical insights for designing transparent and interpretable human-AI interactions in code generation.

Alexandrina Deaconu, Anubhav Gupta, Manaal Basha et al. · 0 citations

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