Reducing Hallucination

Large language models (LLMs) are prone to "hallucinations"—generating factually incorrect or nonsensical information. Current research focuses on mitigating this issue through various techniques, including grounding LLM responses in external knowledge bases (e.g., using Retrieval Augmented Generation), improving uncertainty estimation within the models themselves, and leveraging contrastive learning or multi-agent debate frameworks to refine model outputs. Successfully reducing hallucinations is crucial for increasing the reliability and trustworthiness of LLMs across diverse applications, from question answering and summarization to multimodal tasks involving image and text.