18 citations · 27 across the 9 of their papers we have counts for
5 papers · 1 filter
Open (Clinical) LLMs are Sensitive to Instruction Phrasings
Alberto Mario Ceballos Arroyo, Monica Munnangi, Jiuding Sun +4
Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are p…
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…
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges
Sanjana Ramprasad, Denis Jered McInerney, Iain J. Marshal +1
We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on…
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…
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Denis Jered McInerney, Luyang Kong, Kristjan Arumae +2
Multi-task learning is useful in NLP because it is often practically desirable to have a single model that works across a range of tasks. In the medical domain, sequential training…