Fairness in Large Language Models in Three Hours

For example, one line of work extends traditional fairness in LLMs involves unique backgrounds, taxonomies, and fairness notions--individual fairness and group fairness--to these fulfillment techniques. This tutorial provides a systematic overview models[6]. Specifically, individual fairness seeks to ensure similar of recent advances in the literature concerning fair LLMs, beginning outcomes for similar individuals [13, 49], while group fairness focuses with real-world case studies to introduce LLMs, followed by on equalizing outcome statistics across subgroups defined by an analysis of bias causes therein. The concept of fairness in LLMs sensitive attributes [18, 44-46] (e.g., gender or race). While these is then explored, summarizing the strategies for evaluating bias classification-based fairness notions are adept at evaluating bias in and the algorithms designed to promote fairness. Additionally, resources LLM's classification results[6], they fall short in addressing biases for assessing bias in LLMs, including toolkits and datasets, that arise during the LLM generation process[20].