Dynamic Supplementation of Federated Search Results for Reducing Hallucinations in LLMs

The increasing use of AI-generated content has highlighted the critical issue of hallucinations, where models produce factually incorrect or misleading outputs. Addressing this challenge, a novel approach dynamically supplements federated search engine results in real-time to significantly reduce hallucinations and enhance response accuracy. The methodology involves integrating real-time data from multiple search engines into the responses generated by the Mistral Large model, thereby providing a more accurate and contextually appropriate output. Comprehensive evaluation using the Microsoft PromptBench dataset demonstrates substantial improvements in accuracy, relevance, and reduction of hallucinations. Quantitative performance metrics, statistical analysis, and detailed case studies confirm the effectiveness of the dynamic supplementation approach. The findings suggest significant implications for developing more reliable AI applications across various domains, emphasizing the potential for hybrid systems that combine the strengths of large language models and real-time information retrieval. Future research directions include refining triggering mechanisms, expanding data sources, and optimizing the supplementation process to further enhance performance and scalability.