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arXiv Computation and Language By Ian Porada, Suraj Tripathi, Chaitanya Animesh, Leda Sari, Chaitanya Narisetty, Austin R. Matthews, Bohan Yu, Ramesh L. Thulasiram, Gregory Sell, Kean Chin, Dmitriy Genzel

OSynth: A Longitudinal, Context-Sensitive Evaluation of Meeting Understanding

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OSynth is a large-scale synthetic dataset that simulates organizations, providing 570 hours of meeting audio along with related artifacts and a latent ground-truth state. Human raters can only distinguish OSynth meetings from real transcripts 56% of the time when given short excerpts, indicating the dataset’s realism. When tested with state-of-the-art large language models, adding contextual information from OSynth significantly improves performance on entity-sensitive tasks, highlighting both current limitations and the dataset’s potential for advancing contextual meeting understanding.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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