500 citations · 936 across the 43 of their papers we have counts for
56 papers
Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges
Hiba Ahsan, Denis Jered McInerney, Jisoo Kim +4
Unstructured data in Electronic Health Records (EHRs) often contains critical information -- complementary to imaging -- that could inform radiologists' diagnoses. But the large vo…
USB: A Unified Summarization Benchmark Across Tasks and Domains
Kundan Krishna, Prakhar Gupta, Sanjana Ramprasad +3
While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to cont…
Revisiting Relation Extraction in the era of Large Language Models
Somin Wadhwa, Silvio Amir, Byron C. Wallace
Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag token…
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
Junru Lu, Jiazheng Li, Byron C. Wallace +2
Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potenti…
How Many and Which Training Points Would Need to be Removed to Flip this Prediction?
Jinghan Yang, Sarthak Jain, Byron C. Wallace
We consider the problem of identifying a minimal subset of training data such that if the instances comprising had been removed prior to training, t…
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models
Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent +1
We propose CHiLL (Crafting High-Level Latents), an approach for natural-language specification of features for linear models. CHiLL prompts LLMs with expert-crafted queries to gene…