PhD Proposal: Closing the User Expectations Gap: Interpretability and Cultural Extensions to NLP Models

https://umd.zoom.us/j/96571234197
Data-driven approaches, particularly large pre-trained language models (LLMs), have revolutionized natural language processing (NLP) by capturing semantics and memorizing complex structures from extensive unstructured text data, resulting in substantial improvements in NLP applications. However, data-driven models might not always reflect user needs and expectations due to several inherent limitations and gaps, such as bias in training data, lack of interpretability and transparency, over-reliance on common patterns, etc.This proposal closes the gap between data-driven NLP models and user expectations, with a focus on improving interpretability and extending the models' ability to align with culture.