Survey of Large Language Models for Answering Questions Across Various Fields

In recent years, the burgeoning popularity of language models stems from their demonstrably superior performance compared to traditional machine learning techniques and conventional deep learning models across various Natural Language Processing (NLP) tasks. Despite their remarkable achievements, large language models (LLMs) are not immune to constraints, such as sporadic generation of inaccurate responses, particularly noticeable in intricate queries. Presently, LLMs find extensive application across diverse domains, with one prominent example being question-answering tasks, wherein the model is tasked with generating responses based on provided input questions and context. The intricacy escalates when the model operates in a context-agnostic manner. This study presents a thorough examination of question-answering tasks across different domains, including Open domain, Medical domain, Visual, and Multi-lingual question-answering. We delve into the latest advancements in LLMs across these domains, offering nuanced insights into their efficacy and limitations.