3 citations · 3 across the 5 of their papers we have counts for
5 papers
Music Transcription with (Almost) No Supervision
Saebyeol Shin, Chao Wan, Zhenzhen Liu +4
Competitive music transcription models require large amounts of paired audio-score data, which is scarce due to collection costs, alignment difficulty, and copyright restrictions.…
Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks
Haowei Fu, Bo Ni, Han Xu +3
Retrieval-Augmented Generation (RAG) and Supervised Finetuning (SFT) have become the predominant paradigms for equipping Large Language Models (LLMs) with external knowledge for di…
Assessing Factual Music Comprehension in Large Audio Language Models
Daniel Chenyu Lin, Michael Freeman, John Thickstun
Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio. In this paper, we (1) provide empiri…
A Generative Foundation Model for Chest Radiography
Yuanfeng Ji, Dan Lin, Xiyue Wang +12
The scarcity of well-annotated diverse medical images is a major hurdle for developing reliable AI models in healthcare. Substantial technical advances have been made in generative…
olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
Jake Poznanski, Aman Rangapur, Jon Borchardt +6
PDF documents have the potential to provide trillions of novel, high-quality tokens for training language models. However, these documents come in a diversity of types with differi…