Detecting Hallucinations in Textual Data: Insights From Machine Learning Models in Contextual QA

In recent times, contextual question answering has surfaced as a significant area of research within the field of natural language processing (NLP), with applications ranging from virtual assistants to information retrieval systems. However, the accuracy and reliability of such systems heavily depend on their ability to detect and mitigate hallucination, where generated responses deviate from factual correctness. This research carried out a comparative analysis of machine learning algorithms aimed at identifying hallucination in text data, specifically focusing on contextual question answering. A diverse dataset comprising contextual information, questions, and corresponding answers, annotated with hallucination labels was utilized. The approach encompasses preprocessing textual data through advanced NLP methodologies, including tasks like tokenization, lemmatization, PoS tagging, parsing, NER recognition, and TF-IDF vectorization. Our findings reveal insights into the efficacy of different machine learning approaches in detecting hallucination in contextual question answering scenarios, contributing to the advancement of NLP research and the development of more reliable question answering systems.