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Researcher profile

Lijie Wen

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Learning Algebraic Recombination for Compositional Generalization

    2021 · arXiv (Cornell University)

    Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. Compositional generalization requires algebraic recombination, i.e., dynamically recombining structured expressions in a recursive manner. However, most previous studies mainly concentrate on recombining lexical …

  2. 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 …

  3. 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. …

  4. FSMR: A Feature Swapping Multi-modal Reasoning Approach with Joint Textual and Visual Clues

    2024 · arXiv (Cornell University)

    Multi-modal reasoning plays a vital role in bridging the gap between textual and visual information, enabling a deeper understanding of the context. This paper presents the Feature Swapping Multi-modal Reasoning (FSMR) model, designed to enhance …

  5. Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation

    2024

    Aiwei Liu, Haoping Bai, Zhiyun Lu, Xiang Kong, Xiaoming Wang, Jiulong Shan, Meng Cao, Lijie Wen. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

  6. 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 …