Xuming Hu

Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks

2022 · arXiv (Cornell University)

Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks). Existing augmentation techniques either manipulate the words in the original text …

Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction

2023

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity …

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

2023 · arXiv (Cornell University)

How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for addressing the pervasive data scarcity problem in real-world scenarios. …

ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models

2024 · arXiv (Cornell University)

Despite the recent breakthroughs achieved by Large Vision Language Models (LVLMs) in understanding and responding to complex visual-textual contexts, their inherent hallucination tendencies limit their practical application in real-world scenarios that demand high levels of …