Pre-training large language models (LLMs) on vast text corpora enhances natural language processing capabilities but risks encoding social biases, particularly gender bias. While parameter-modification methods like fine-tuning mitigate bias, they are resource-intensive, unsuitable for closed-source models, and lack adaptability to evolving societal norms. Instruction-based approaches offer flexibility but often compromise task performance. To address these limitations, we propose $\textit{FaIRMaker}$, an automated and model-independent framework that employs an $\textbf{auto-search and refinement}$ paradigm to adaptively generate Fairwords, which act as instructions integrated into input queries to reduce gender bias and enhance response quality. Extensive experiments demonstrate that $\textit{FaIRMaker}$ automatically searches for and dynamically refines Fairwords, effectively mitigating gender bias while preserving task integrity and ensuring compatibility with both API-based and open-source LLMs.

Did you miss a session at the Data Summit? Harassment in the workplace affects employees of all backgrounds, genders, sexualities, and ethnicities -- but disproportionately those in under-represented groups. A 2018 survey by Stop Street Harassment showed that 81% of women have been harassed in their lifetime. And according to a UCLA School of Law study, half of LGBTQ workers have faced job discrimination at some point in their careers. Work-from-home arrangements during the pandemic haven't slowed or reversed the trend -- in fact, they've accelerated it.