A popular approach to High-Recall Information Retrieval (HRIR) is Technology-Assisted
Review (TAR), which uses information retrieval and machine learning techniques to aid the …

In high recall retrieval tasks, human experts review a large pool of documents with the goal
of satisfying an information need. Documents are prioritized for review through an active …

Technology-assisted review (TAR) refers to iterative active learning workflows for document
review in high recall retrieval (HRR) tasks. TAR research and most commercial TAR …

HC4 is a new suite of test collections for ad hoc Cross-Language Information Retrieval
(CLIR), with Common Crawl News documents in Chinese, Persian, and Russian, topics in …

Technology-assisted Review (TAR), which aims to reduce the effort required to screen
collections of documents for relevance, is used to develop systematic reviews of medical …

Screening documents is a tedious and time-consuming aspect of high-recall retrieval tasks,
such as compiling a systematic literature review, where the goal is to identify all relevant …

The First Search Futures Workshop, in conjunction with the Fourty-sixth European
Conference on Information Retrieval (ECIR) 2024, looked into the future of search to ask …

Search methods based on Pretrained Language Models (PLM) have demonstrated great
effectiveness gains compared to statistical and early neural ranking models. However, fine …

We present RLStop, a novel Technology Assisted Review (TAR) stop** rule based on
reinforcement learning that helps minimise the number of documents that need to be …

The rapid advancement of Generative Artificial Intelligence (AI), particularly Large Language
Models (LLMs), has led to their widespread adoption for various natural language …