@inproceedings{wang-etal-2025-hmcl,
    title = "{HMCL}: Task-Optimal Text Representation Adaptation through Hierarchical Contrastive Learning",
    author = "Wang, Zhenyi  and
      Jia, Yapeng  and
      Ning, Haiyan  and
      Wang, Peng  and
      Wang, Dan  and
      Cao, Yitao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.727/",
    doi = "10.18653/v1/2025.findings-emnlp.727",
    pages = "13495--13518",
    ISBN = "979-8-89176-335-7",
    abstract = "As general large language models continue to advance, their real-world adaptation through effective fine-tuning remains a significant challenge. We introduce Hierarchical Multilevel Contrastive Learning (HMCL), a new contrastive learning framework that improves task-specific text representation for general models. HMCL integrates 3-level semantic differentiation (positive, weak-positive, and negative) and unifies contrastive learning, pair classification, and ranking objectives into a cohesive optimization strategy. HMCL demonstrates exceptional results across multi-domain and multilingual benchmarks, including text similarity, retrieval, reranking and Retrieval-Augmented Generation (RAG) tasks. It outperforms top unsupervised methods and supervised fine-tuning approaches while maintaining broad compatibility with architectures ranging from BERT to Qwen, 330M to 7B. In real-world merchant consultation scenarios, HMCL shows a 0.70-6.24 point improvement over original fine-tuning methods in large-scale base models. This establishes HMCL as a versatile solution that bridges the gap between general-purpose models and specialized industrial applications."
}