Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification

arXiv:2402.18502v1 Announce Type: new
Abstract: Employing Large Language Models (LLM) in various downstream applications such as classification is crucial, especially for smaller companies lacking the expertise and resources required for fine-tuning a model. Fairness in LLMs helps ensure inclusivity, equal representation based on factors such as race, gender and promotes responsible AI deployment. As the use of LLMs has become increasingly prevalent, it is essential to assess whether LLMs can generate fair outcomes when subjected to considerations of fairness. In …

abstract applications arxiv classification companies cs.cl expertise fairness few-shot fine-tuning gender inclusivity language language models large language large language models llm llms race representation resources responsible responsible ai type