Paper Contents
Abstract
Natural Language Processing (NLP) models have gotten more complicated and widely used over time, making them challenging to understand and comprehend. The purpose of this study is to look into ways to improve the explainability and interpretability of complicated NLP models. We want to improve these modelstransparency and reliability by bridging the gap between human knowledge and model performance. We will investigate a range of strategies to clarify how these models interpret and produce language, such as feature attribution approaches, model distillation, and visualization tools. We will also look at explainability's place in moral AI procedures, with an emphasis on minimizing prejudice and guaranteeing equity. In the end, our study will promote increased acceptance and responsible use of AI technology in real-world applications by offering actionable insights and practical instructions for constructing interpretable and explainable NLP models through extensive experiments and case studies.
Copyright
Copyright © 2024 Ms. Nitika Kadam1 . This is an open access article distributed under the Creative Commons Attribution License.
