Zifeng Wang

Orcid: 0000-0002-3026-9970

Affiliations:
  • University of Illinois Urbana-Champaign, IL, USA


According to our database1, Zifeng Wang authored at least 15 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Online presence:

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Bibliography

2024
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale.
CoRR, 2024

GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models.
CoRR, 2024

PILOT: Legal Case Outcome Prediction with Case Law.
CoRR, 2024

2023
UniPredict: Large Language Models are Universal Tabular Predictors.
CoRR, 2023

CITING: Large Language Models Create Curriculum for Instruction Tuning.
CoRR, 2023

MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models.
CoRR, 2023

Matching Patients to Clinical Trials with Large Language Models.
CoRR, 2023

PyTrial: A Comprehensive Platform for Artificial Intelligence for Drug Development.
CoRR, 2023

AnyPredict: Foundation Model for Tabular Prediction.
CoRR, 2023

TWIN: Personalized Clinical Trial Digital Twin Generation.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

AutoTrial: Prompting Language Models for Clinical Trial Design.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning.
Proceedings of the 14th ACM International Conference on Bioinformatics, 2023

2022
TransTab: Learning Transferable Tabular Transformers Across Tables.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022


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