Web Science and Digital Libraries Research Group

  Fig 1 from Rawte et al. Taxonomy for Hallucination in Large Foundation Model The rise of large language models (LLMs) has brought about accelerated advances in natural language processing (NLP), enabling powerful results in text generation, comprehension, and reasoning. However, alongside these advancements comes a persistent and critical issue: hallucination. Defined as the generation of content that deviates from factual accuracy or the provided input, hallucination presents a multifaceted challenge with implications across various domains, from journalism to healthcare. This blog post presents insights from three recent comprehensive surveys on hallucination in natural language generation (NLG) and foundation models to provide an understanding of the problem, its causes, and ongoing mitigation efforts. “ Survey of Hallucination in Natural Language Generation ” by Ji et al. (2022) provides a foundational exploration of hallucination in various NLG tasks, including abstractiv...

  Comparison of Standard RAG systems and MemoRAG ( Qian et al. ) In my post “ ALICE - AI Leveraged Information Capture and Exploration ” , I proposed a system that unifies a Large Language Model (LLM) with a knowledge graph (KG) to archive routinely lost information from literature generating events. While assessing project risks, we have further researched hallucination and semantic sprawl mitigation strategies. I have been focusing on representation learning and embedding space methods to reduce the need for external knowledge bases such as in Retrieval Augmented Generation (RAG) methods. Along the way, I discovered similar current research. In this post, we review “ MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery ”, a novel approach to RAG by Hongjin Qian, Peitian Zhang, and Zheng Liu from the Beijing Academy of Artificial Intelligence; and Kelong Mao and Zhicheng Dou from Renmin University of China published in the ACM Web Conference 2025 . MemoRAG...