3 accepted papers
Large Language Models (LLMs) have advanced Natural Language Processing (NLP) tasks but are limited in mathematical reasoning. To address this, few-shot examples are used in prompts for in-context learning. However, existing methods require annotated datasets, resulting in higher computational costs…
Deep learning models such as CNNs have surpassed human performance in computer vision tasks such as image classi- fication. However, despite their sophistication, these models lack interpretability which can lead to biased outcomes re- flecting existing prejudices in the data. We aim to make pre- di…
Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic repr…