Uncovering and Addressing Bias in LLM Interactions


Project Description

Large Language Models (LLMs) have become ubiquitous in contemporary applications. They are trained on extensive collections of human writings, ranging from books, papers, and news articles to conversations on social media platforms. This comprehensive approach enables the development of sophisticated tools capable of emulating human interactions with remarkable fidelity. However, it is important to recognize that LLMs might inherit and perpetuate the biases inherent in human communications. This project represents a concerted effort to delve deeply into the multifaceted landscape of biases inherent in interactions with LLM agents. By examining various dimensions of biases, we aim to explore how these biases manifest within LLM-mediated interactions. Through this project, we would not only understand the complexities of bias within LLM interactions, but also explore the possibility to mitigate, neutralize, or rectify these biases. This will underline if LLMs are more inclined to change opinions than humans. This approach underscores our commitment to fostering fairness, equity, and inclusivity in the realm of LLM-driven communication and interaction, ultimately advancing the societal impact and ethical integrity of LLM technology.
Program - Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link - https://cri-lab.net
Center Affiliation - Resilient Computing and Cybersecurity Center
Field of Study - Computer Science

About the

Roberto Di Pietro

Professor, Computer Science

Roberto Di Pietro

Desired Project Deliverables

The project anticipates two primary outcomes: the creation of a system capable of simulating interactions within an online platform using LLMs as agents, and the utilization of the system to gain knowledge on the biases exhibited by LLMs across various operational scenarios.


MS in Computer Science/Computer Engineering or final year MS
MS in Computer Science/Computer Engineering or final year MS
Currently PhD student
Currently PhD student