@inproceedings{radevski-etal-2025-synthesizing,
    title = "On Synthesizing Data for Context Attribution in Question Answering",
    author = "Radevski, Gorjan  and
      Gashteovski, Kiril  and
      Syed, Shahbaz  and
      Malon, Christopher  and
      Nicolas, Sebastien  and
      Hung, Chia-Chien  and
      Sztyler, Timo  and
      Heu{\ss}er, Verena  and
      Ben Rim, Wiem  and
      Enomoto, Masafumi  and
      Takeoka, Kunihiro  and
      Oyamada, Masafumi  and
      Glava{\v{s}}, Goran  and
      Lawrence, Carolin",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.828/",
    pages = "16929--16950",
    ISBN = "979-8-89176-251-0",
    abstract = "Question Answering (QA) accounts for a significant portion of LLM usage in the wild''. However, LLMs sometimes produce false or misleading responses, also known as hallucinations''. Therefore, grounding the generated answers in contextually provided information{---}i.e., providing evidence for the generated text{---}is paramount for LLMs' trustworthiness. Providing this information is the task of context attribution. In this paper, we systematically study LLM-based approaches for this task, namely we investigate (i) zero-shot inference, (ii) LLM ensembling, and (iii) fine-tuning of small LMs on synthetic data generated by larger LLMs. Our key contribution is SynQA: a novel generative strategy for synthesizing context attribution data. Given selected context sentences, an LLM generates QA pairs that are supported by these sentences. This leverages LLMs' natural strengths in text generation while ensuring clear attribution paths in the synthetic training data. We show that the attribution data synthesized via SynQA is highly effective for fine-tuning small LMs for context attribution in different QA tasks and domains. Finally, with a user study, we validate the usefulness of small LMs (fine-tuned on synthetic data from SynQA) in context attribution for QA."
}