Papers by Hejie Cui

2 papers
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction (2023.findings-acl)

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Challenge: Empirical results show up to 11.74% absolute (20.97% relative) increase over unimodal baselines.
Approach: They propose to patch the visual modality to the textual-established attribute in- formation extractor.
Outcome: Empirical results show up to 11.74% absolute (29.9% relative) increase over unimodal baselines.
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)

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Challenge: Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text.
Approach: They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks.
Outcome: The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average.

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