Mitigating Factual Inconsistency and Hallucination in Large Language Models

Large Language Models (LLMs) have demonstrated remarkable capabilities in various language-related tasks enabling applications in various fields such as healthcare, education, financial services etc. However, they are prone to producing factually incorrect responses or ''hallucinations'' which can have detrimental consequences such as loss of credibility, diminished customer trust etc. In this presentation, we showcase a solution that addresses the challenge of minimizing hallucinations. Our solution provides accurate responses and generates detailed explanations, thereby enabling the users to know how the model arrived at the final response. Additionally, it verifies if the explanations are factually correct and offers insights into whether the generated explanations are directly derived from the provided context or if they are inferred from it. We also systematically assess the quality of generated responses using an LLM-based evaluation technique. We present empirical results on benchmark datasets to demonstrate the effectiveness of our approach. Our presentation also examines the impact of individual components in the solution, enhancing the factual correctness of the final response. This research is vital for industries utilizing LLMs, as it provides a means to enhance the reliability of responses and mitigate the risks associated with factual hallucinations. Researchers and practitioners seeking to enhance the reliability of LLM responses will find valuable insights in this presentation.