38 citations · 50 across the 6 of their papers we have counts for
6 papers
On-the-fly Definition Augmentation of LLMs for Biomedical NER
Monica Munnangi, Sergey Feldman, Byron C Wallace +3
Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In th…
NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs
Maria Antoniak, Aakanksha Naik, Carla S. Alvarado +2
Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applicat…
LongBoX: Evaluating Transformers on Long-Sequence Clinical Tasks
Mihir Parmar, Aakanksha Naik, Himanshu Gupta +2
Many large language models (LLMs) for medicine have largely been evaluated on short texts, and their ability to handle longer sequences such as a complete electronic health record…
S2abEL: A Dataset for Entity Linking from Scientific Tables
Yuze Lou, Bailey Kuehl, Erin Bransom +3
Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications. When app…
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Kyle Lo, Joseph Chee Chang, Andrew Head +52
Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature…
Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections
Srishti Palani, Aakanksha Naik, Doug Downey +3
Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes…