67 citations · 91 across the 8 of their papers we have counts for
10 papers
Comparing GenAI to Machine Learning and Manual Review for eDiscovery
Gordon V. Cormack, Maura R. Grossman, Jeremy Pickens
Keeling et al. [Keeling2026Generative] report on a case study recently conducted by Redgrave LLP in which they assert that Relativity's Generative AI Review tool (aiR) achieved 88%…
Continuous Active Learning Using Pretrained Transformers
Nima Sadri, Gordon V. Cormack
Pre-trained and fine-tuned transformer models like BERT and T5 have improved the state of the art in ad-hoc retrieval and question-answering, but not as yet in high-recall informat…
The Absurdity of Death Estimates Based on the Vaccine Adverse Event Reporting System
Gordon V Cormack, Maura R Grossman
We demonstrate from first principles a core fallacy employed by a coterie of authors who claim that data from the Vaccine Adverse Reporting System (VAERS) show that hundreds of tho…
The eDiscovery Medicine Show
Maura R. Grossman, Gordon V. Cormack
The practice of bloodletting gradually fell into disfavor as a growing body of scientific evidence showed its ineffectiveness and demonstrated the effectiveness of various pharmace…
Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval
Haotian Zhang, Gordon V. Cormack, Maura R. Grossman +1
This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer wi…
Impact of Feature Selection on Micro-Text Classification
Ankit Vadehra, Maura R. Grossman, Gordon V. Cormack
Social media datasets, especially Twitter tweets, are popular in the field of text classification. Tweets are a valuable source of micro-text (sometimes referred to as "micro-blogs…