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13Addressing Ethical Challenges in LLMs: Bias and Misinformation

Pummy Dhiman* and Amandeep Kaur

Chitkara University Institute of Engineering and Technology Chitkara University, Punjab, India

Abstract

The advent of large language models (LLMs) has had a significant and transformative impact in the natural language processing domain. There is no doubt that AI has been used in many sectors, including customer services, finance, healthcare, academia, and many others. However, the potential of these models can be compromised, as they can generate output that is biased and even generate misinformation. The present chapter offers a comprehensive analysis of the ethical quandaries that arise due to security vulnerabilities linked to LLMs. The primary objective of this study is to explore the architecture and characteristics of LLM that contribute to the continuation of existing imbalances and the dissemination of harmful information. Furthermore, this study emphasizes the importance of addressing these ethical considerations to mitigate adverse effects on individuals and society. In order to enhance equity and dependability inside AI systems, it is imperative to acknowledge and mitigate bias and misinformation within language models.

Keywords: LLM, misinformation, fake news, bias, ethical, GPT, BERT, artificial intelligence, transformers

13.1 Introduction

Transformer [1, 2], a revolutionary framework in the field of deep learning (DL) [3], brought about a significant change in the ...