@conference {1613,
	title = {An Overview of Recent Approaches to Enable Diversity in Large Language Models through Aligning with Human Perspectives},
	booktitle = {Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024},
	year = {2024},
	publisher = {ELRA and ICCL},
	organization = {ELRA and ICCL},
	address = {Torino, Italia},
	abstract = {The varied backgrounds and experiences of human annotators inject different opinions and potential biases into the data, inevitably leading to disagreements. Yet, traditional aggregation methods fail to capture individual judgments since they rely on the notion of a single ground truth. Our aim is to review prior contributions to pinpoint the shortcomings that might cause stereotypical content generation. As a preliminary study, our purpose is to investigate state-of-the-art approaches, primarily focusing on the following two research directions. First, we investigate how adding subjectivity aspects to LLMs might guarantee diversity. We then look into the alignment between humans and LLMs and discuss how to measure it. Considering existing gaps, our review explores possible methods to mitigate the perpetuation of biases targeting specific communities. However, we recognize the potential risk of disseminating sensitive information due to the utilization of socio-demographic data in the training process. These considerations underscore the inclusion of diverse perspectives while taking into account the critical importance of implementing robust safeguards to protect individuals{\textquoteright} privacy and prevent the inadvertent propagation of sensitive information.},
	url = {https://aclanthology.org/2024.nlperspectives-1.5/},
	author = {Muscato, Benedetta and Mala, Chandana Sree and Marchiori Manerba, Marta and Gezici, Gizem and Giannotti, Fosca},
	editor = {Abercrombie, Gavin and Basile, Valerio and Bernadi, Davide and Dudy, Shiran and Frenda, Simona and Havens, Lucy and Tonelli, Sara}
}
