7 citations · 13 across the 8 of their papers we have counts for
4 papers · 1 filter
Data-Centric Lessons To Improve Speech-Language Pretraining
Vishaal Udandarao, Zhiyun Lu, Xuankai Chang +6
Spoken Question-Answering (SQA) is a core capability for useful and interactive artificial intelligence systems. Recently, several speech-language models (SpeechLMs) have been rele…
OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
Huong Ngo, Matt Deitke, Martijn Bartelds +4
Improvements in training data scale and quality have led to significant advances, yet its influence in speech recognition remains underexplored. In this paper, we present a large-s…
Language Models Improve When Pretraining Data Matches Target Tasks
David Mizrahi, Anders Boesen Lindbo Larsen, Jesse Allardice +7
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies…
UTSA-NLP at ArchEHR-QA 2025: Improving EHR Question Answering via Self-Consistency Prompting
Sara Shields-Menard, Zach Reimers, Joshua Gardner +2
We describe our system for the ArchEHR-QA Shared Task on answering clinical questions using electronic health records (EHRs). Our approach uses large language models in two steps:…