7 citations · 19 across the 22 of their papers we have counts for
6 papers · 1 filter
Compositional Representation of Polymorphic Crystalline Materials
Namkyeong Lee, Heewoong Noh, Gyoung S. Na +4
Machine learning (ML) has seen promising developments in materials science, yet its efficacy largely depends on detailed crystal structural data, which are often complex and hard t…
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…
CITING: Large Language Models Create Curriculum for Instruction Tuning
Tao Feng, Zifeng Wang, Jimeng Sun
The recent advancement of large language models (LLMs) has been achieved through a combo of instruction tuning and human alignment. However, building manually crafted instruction d…
Molecular De Novo Design through Transformer-based Reinforcement Learning
Pengcheng Xu, Tao Feng, Tianfan Fu +2
In this work, we introduce a method to fine-tune a Transformer-based generative model for molecular de novo design. Leveraging the superior sequence learning capacity of Transforme…
UniPredict: Large Language Models are Universal Tabular Classifiers
Ruiyu Wang, Zifeng Wang, Jimeng Sun
Tabular data prediction is a fundamental machine learning task for many applications. Existing methods predominantly employ discriminative modeling and operate under the assumption…
DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients
Hanyin Wang, Chufan Gao, Christopher Dantona +2
In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient. The study introduces DRG-LLaMA, an advanced large lan…