Researching the challenges and risk of using large language models in Natural Language Processing (NLP), focusing on addressing these issues through the use of explainable NLP.
There has been significant progress in NLP in recent years, particularly with the development of pre-trained and large language models. These models are based on neural networks and are trained on massive amounts of text data, allowing them to learn the patterns and structure of language in a way that mimics human understanding. However, the increased use of these types of models has highlighted many challenges in NLP that still need to be addressed. Issues such as bias and lack of model interpretability are important considerations in the development and deployment of NLP models.
Natural Language Processing, Explainable AI, Interpretability, Reasoning, Bias, Hallucinations
BEng Electronic Engineering, University of Southampton
MSc Digital and Technology Solutions Specialist, Aston University
Level 7 Apprenticeship Digital and Technology Solutions Specialist, Aston University
Four years working in industry as a Data Scientist
Dr Harish Tayyar Madabushi
Dr Iulia Cioroianu
Dr Claire Bonial (Georgetown University and US Army DEVCOM)
