Towards detecting unanticipated bias in Large Language Models

arXiv:2404.02650v1 Announce Type: new
Abstract: Over the last year, Large Language Models (LLMs) like ChatGPT have become widely available and have exhibited fairness issues similar to those in previous machine learning systems. Current research is primarily focused on analyzing and quantifying these biases in training data and their impact on the decisions of these models, alongside developing mitigation strategies. This research largely targets well-known biases related to gender, race, ethnicity, and language. However, it is clear that LLMs are also …

abstract arxiv become bias biases chatgpt cs.ai cs.cl cs.lg current data decisions fairness impact language language models large language large language models learning systems llms machine machine learning research systems training training data type