Allen School researchers earn EMNLP Best Paper Award for making Internet-scale texts efficiently searchable with infini-gram mini

As large language models become more popular and widespread, it becomes increasingly important to understand the composition of their training data and how it affects the model’s behavior. The first step is to make these texts searchable. To make searching on such a large scale more efficient and affordable, researchers at the University of Washington and Allen Institute for Artificial Intelligence (Ai2) developed infini-gram mini , an efficient search engine designed to handle exact-match search on arbitrarily long queries across Internet-scale corpora with minimal storage overhead. The team received the Best Paper Award at the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP) last November. Read more →