Zero-shot Audio Topic Reranking using Large Language Models
Mengjie Qian Rao Ma Adian Liusie Erfan Loweimi Kate M. Knill Mark J. F. Gales
The Multimodal Video Search by Examples (MVSE) project investigates using
video clips as the query term for information retrieval, rather than the more
traditional text query. This enables far richer search modalities such as
images, speaker, content, topic, and emotion. A key element for this process is
highly rapid, flexible, search to support large archives, which in MVSE is
facilitated by representing video attributes by embeddings. This work aims to
mitigate any performance loss from this rapid archive search by examining
reranking approaches. In particular, zero-shot reranking methods using large
language models are investigated as these are applicable to any video archive
audio content. Performance is evaluated for topic-based retrieval on a publicly
available video archive, the BBC Rewind corpus. Results demonstrate that
reranking can achieve improved retrieval ranking without the need for any
task-specific training data.