Detecting and correcting Factual Error in LLM Text series: Review
TL;DR
This thesis begins by establishing the background and motivation for the study, focusing on the growing importance of automatic text summarization and the challenges associated with factual inconsistencies in generated summaries, and critically reviews prior research in the field of text summarization.
Abstract
This thesis presents a detailed introduction to the research work. It begins by establishing the background and motivation for the study, focusing on the growing importance of automatic text summarization and the challenges associated with factual inconsistencies in generated summaries. The chapter critically reviews prior research in the field of text summarization, highlighting the limitations of traditional Automatic Text Summarization (ATS) systems, particularly in ensuring factual correctness. Furthermore, the chapter introduces Large Language Models (LLMs) and prompt engineering as emerging solutions capable of addressing these limitations. The objectives of the research, along with the key research questions, are clearly articulated to define the scope and direction of the study. The chapter also delineates the boundaries of the research by specifying the scope and assumptions considered. Finally, the significance of the study is discussed, emphasizing its contribution to improving evaluation methodologies for text summarization systems. . Key Words: Natural Language Processing (NLP), Large Language Models (LLM), Chain of Thought (COT), Generative Pre-trained Transformers (GPT), Recall-Oriented Understudy