2 citations · 6 across the 11 of their papers we have counts for
4 papers · 1 filter
Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise
Hanyin Wang, Chufan Gao, Qiping Xu +9
Process-supervised reward models (PRMs) excel at providing step-by-step verification for large language model (LLM) outputs in domains like mathematics and coding. However, their a…
TTM-RE: Memory-Augmented Document-Level Relation Extraction
Chufan Gao, Xuan Wang, Jimeng Sun
Document-level relation extraction aims to categorize the association between any two entities within a document. We find that previous methods for document-level relation extracti…
Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation
Hanyin Wang, Chufan Gao, Bolun Liu +7
Proprietary Large Language Models (LLMs) such as GPT-4 and Gemini have demonstrated promising capabilities in clinical text summarization tasks. However, due to patient data privac…
PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Chufan Gao, Xulin Fan, Jimeng Sun +1
Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extra…