Correcting Hallucinations in Large Language Models

Hallucination in large language models (LLMs) refers to instances where the model generates information not based on provided data, posing challenges, especially in enterprise applications. The Hallucination Correction Model (HCM) is a post-editing tool developed to address this by correcting such inaccuracies in generated responses without altering the overall structure. It operates by comparing the generated response with reference documents to isolate and correct fabricated information. Evaluations using various datasets, including FAVABENCH, NonFactS, and RAGTruth, demonstrate that HCM significantly improves the factuality rate of responses across different models, although challenges remain with models like Falcon-7B-Instruct, which often deviate from source material. While HCM does not completely eliminate hallucinations, its application shows promise in enhancing the reliability of LLM outputs, particularly in retrieval-augmented generation (RAG) contexts. The study highlights both the progress made and areas needing further exploration, signaling a positive direction in reducing LLM hallucinations, with ongoing efforts to refine the model for broader applicability.