Lotem Golany
2024
Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts
Lotem Golany
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Filippo Galgani
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Maya Mamo
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Nimrod Parasol
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Omer Vandsburger
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Nadav Bar
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Ido Dagan
Findings of the Association for Computational Linguistics: EMNLP 2024
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.
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Co-authors
- Filippo Galgani 1
- Maya Mamo 1
- Nimrod Parasol 1
- Omer Vandsburger 1
- Nadav Bar 1
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