244 citations · 508 across the 40 of their papers we have counts for
8 papers · 1 filter
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…
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…
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
Yilin Wen, Zifeng Wang, Jimeng Sun
Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks. However, they often suffer from limitations such as diffic…
Matching Patients to Clinical Trials with Large Language Models
Qiao Jin, Zifeng Wang, Charalampos S. Floudas +7
Patient recruitment is challenging for clinical trials. We introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models. TrialGPT…
PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications
Zifeng Wang, Brandon Theodorou, Tianfan Fu +2
Clinical trials are conducted to test the effectiveness and safety of potential drugs in humans for regulatory approval. Machine learning (ML) has recently emerged as a new tool to…
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
Zifeng Wang, Chufan Gao, Cao Xiao +1
Tabular data prediction has been employed in medical applications such as patient health risk prediction. However, existing methods usually revolve around the algorithm design whil…
AutoTrial: Prompting Language Models for Clinical Trial Design
Zifeng Wang, Cao Xiao, Jimeng Sun
Clinical trials are critical for drug development. Constructing the appropriate eligibility criteria (i.e., the inclusion/exclusion criteria for patient recruitment) is essential f…
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning
Zifeng Wang, Cao Xiao, Jimeng Sun
Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. Accurate trial outcome prediction based on historical trial data promises better…