arXiv (All)
open access
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 pro
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation#798985
- ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation#797571
- PlayCoder: Making LLM-Generated GUI Code Playable#1131509
- A systematic evaluation of large language models of code#16618
- Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality#373456
Record · ID 1036786
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive