@inproceedings{golany-etal-2024-efficient,
    title = "Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts",
    author = "Golany, Lotem  and
      Galgani, Filippo  and
      Mamo, Maya  and
      Parasol, Nimrod  and
      Vandsburger, Omer  and
      Bar, Nadav  and
      Dagan, Ido",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.findings-emnlp.106/",
    doi = "10.18653/v1/2024.findings-emnlp.106",
    pages = "1908--1925",
    abstract = "Automating data generation with Large Language Models (LLMs) has become increasingly popular. In this work, we investigate the feasibility and effectiveness of LLM-based data generation in the challenging setting of source-grounded information-seeking dialogs, with response attribution, over long documents. Our source texts consist of long and noisy meeting transcripts, adding to the task complexity. Since automating attribution remains difficult, we propose a semi-automatic approach: dialog queries and responses are generated with LLMs, followed by human verification and identification of attribution spans. Using this approach, we created MISeD {--} Meeting Information Seeking Dialogs dataset {--} a dataset of information-seeking dialogs focused on meeting transcripts. Models finetuned with MISeD demonstrate superior performance compared to off-the-shelf models, even those of larger size. Finetuning on MISeD gives comparable response generation quality to finetuning on fully manual data, while improving attribution quality and reducing time and effort."
}