@inproceedings{pal-etal-2025-tagging,
    title = "Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts",
    author = "Pal, Anwesan  and
      Hovsepian, Karen  and
      Guo, Tinghao  and
      Zhao, Mengnan  and
      Tripathi, Somendra  and
      Kanakaris, Nikos  and
      Mihaila, George  and
      Nigam, Sumit",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.153/",
    pages = "2209--2220",
    ISBN = "979-8-89176-333-3",
    abstract = "Recent investigations into effective context lengths of modern flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models. While approaches like retrieval-augmented generation (RAG) and chunk-based re-ranking attempt to mitigate this issue, they are sensitive to chunking, embedding and retrieval strategies and models, and furthermore, rely on extensive pre-processing, knowledge acquisition and indexing steps. In this paper, we propose Tagging-Augmented Generation (TAG), a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios, without degrading and altering the integrity and composition of retrieved documents. We validate our hypothesis by augmenting two challenging and directly relevant question-answering benchmarks {--} NoLima and NovelQA {--} and show that tagging the context or even just adding tag definitions into QA prompts leads to consistent relative performance gains over the baseline {--} up to 17{\%} for 32K token contexts, and 2.9{\%} in complex reasoning question-answering for multi-hop queries requiring knowledge across a wide span of text."
}