@inproceedings{abbes-etal-2025-small,
    title = "Small Encoders Can Rival Large Decoders in Detecting Groundedness",
    author = "Abbes, Istabrak  and
      Prato, Gabriele  and
      Fournier, Quentin  and
      Rodriguez, Fernando  and
      Boukhary, Alaa  and
      Elwood, Adam  and
      Chandar, Sarath",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.1134/",
    pages = "21998--22005",
    ISBN = "979-8-89176-256-5",
    abstract = "Augmenting large language models (LLMs) with external context significantly improves their performance in natural language processing (NLP) tasks. However, LLMs struggle to answer queries reliably when the provided context lacks information, often resorting to ungrounded speculation or internal knowledge. Groundedness {--} generating responses strictly supported by the context {--} is essential for ensuring factual consistency and trustworthiness. This study focuses on detecting whether a given query is grounded in a document provided in context before the costly answer generation by LLMs. Such a detection mechanism can significantly reduce both inference time and resource consumption. We show that lightweight, task-specific encoder models such as RoBERTa and NomicBERT, fine-tuned on curated datasets, can achieve accuracy comparable to state-of-the-art LLMs, such as Llama3 8B and GPT4o, in groundedness detection while reducing inference latency by orders of magnitude."
}