Understanding and Mitigating Strategies for Large Language Model (LLMs) Hallucinations in HR Chatbots

Large language models are widely used in enterprise workflows, particularly in human resources and internal communication using chatbots. Although they provide efficiency and shorter turnaround times, their tendency to hallucinate—generating plausible but factually incorrect information—is a significant concern. This paper provides a comprehensive review of the problem statement and the solutions studied. It starts with defining and evaluating the causes and types of hallucinations particular to HR applications. The research also explores industry use cases and implements mitigating measures such as retrieval-augmented generation (RAG), confidence rating, abstention mechanisms, prompt engineering, domain-specific fine-tuning, and post-generation fact-checking. Using accessible empirical data, the research assesses the limitations, scalability, and effectiveness of various methods. Important research gaps are found, including the absence of HR-specific hallucination benchmarks, difficulties in uncertainty estimates, and the necessity of ongoing domain knowledge integration. Aiming to create reliable and grounded AI systems for HR and corporate support, the article ends by suggesting practical directions for future research and development.