@inproceedings{lu-etal-2025-breaking,
    title = "Breaking the Noise Barrier: {LLM}-Guided Semantic Filtering and Enhancement for Multi-Modal Entity Alignment",
    author = "Lu, Chenglong  and
      Li, Chenxiao  and
      Cheng, Jingwei  and
      Ji, Yongquan  and
      Chen, Guoqing  and
      Zhang, Fu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1684/",
    pages = "33141--33155",
    ISBN = "979-8-89176-332-6",
    abstract = "Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multimodal knowledge graphs (MMKGs). However, the intrinsic noise within modalities, such as the inconsistency in visual modality and redundant attributes, has not been thoroughly investigated. Excessive noise not only weakens semantic representation but also increases the risk of overfitting in attention-based fusion methods. To address this, we propose LGEA, a novel LLMguided MMEA framework that prioritizes noise reduction before fusion. Specifically, LGEA introduces two key strategies: (1) fine-grained visual filtering to remove irrelevant images at the semantic level, and (2) contextual summarization of attribute information to enhance entity semantics. To our knowledge, we are the first work to apply LLMs for both visual filtering and attribute-level semantic enhancement in MMEA. Experiments on multiple benchmarks, including the noisy FB YG dataset, show that LGEA sets a new state-of-the-art (SOTA) in robust multi-modal alignment, highlighting the potential of noise-aware strategies as a promising direction for future MMEA research."
}