@inproceedings{3d40023432ff4203bd5b80d1dc87c76f,
title = "Understanding Gender and Ethnicity Bias in Large Language Models",
abstract = "In this poster, we discuss popular LLM models- in this case LLM Models like LLAMA 2, Gemma and Mistral 7B- for indirect and direct biases through 3 open-sourced and research-oriented datasets with over 1000 prompts in total on fairness factors like gender and ethnicity through prompt injection techniques.",
author = "Ojasvi Gupta and Jaiswal, \{Rajesh R.\} and Stefano Marrone and Lidia Marassi and Francesco Gargiulo",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 2nd Conference on Human Centered Artificial Intelligence - Education and Practice, HCAI-ep 2024 ; Conference date: 01-12-2024 Through 02-12-2024",
year = "2024",
month = dec,
day = "2",
doi = "10.1145/3701268.3701287",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery (ACM)",
pages = "63",
booktitle = "HCAI-ep 2024 - Proceedings of the 2024 Conference on Human Centered Artificial Intelligence - Education and Practice",
address = "United States",
}