Web Science and Digital Libraries Research Group

Artificial intelligence (AI) has transformed nearly every field. Today, we can access and train models that generate text, images, sound, video, and code. This transformation is reshaping how we think, analyze, and preserve information. Yet, despite the rapid growth of AI, its use for analyzing web archive content seems to advance at a slower pace.  Web archiving is the process of collecting, preserving, and providing access to web content over time, where a memento  represents a previous version of a web resource as it existed at a specific moment in the past. Much of the recent work within the web archiving community (e.g., [1] , [2] , [3] ) has focused on making the archiving process itself more intelligent, integrating AI into tasks such as web crawling, storage optimization, and metadata generation. In contrast, the application of AI to the analysis of already archived web content has received comparatively less attention. This gap represents a great opportunity for innov...

  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...