  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.
