Measuring and Mitigating Bias in Code Generated by Large Language Models 事件

Measuring and Mitigating Bias in Code Generated by Large Language Models arXiv:2606.00049v1 Announce Type: cross Abstract: Large language models (LLMs) are widely recognised for their applications in natural language generation and are increasingly used for code generation tasks. However, concerns about bias in their generated outputs remain significant. This paper focuses on GPT-4o and Gemini, mainstream tools for code generation, and proposes a framework for evaluating bias in LLM-generated c