@inproceedings{palma-gomez-etal-2024-transforming,
title = "Transforming {LLM}s into Cross-modal and Cross-lingual Retrieval Systems",
author = "Palma Gomez, Frank and
Sanabria, Ramon and
Sung, Yun-hsuan and
Cer, Daniel and
Dalmia, Siddharth and
Hernandez Abrego, Gustavo",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.4",
pages = "23--32",
abstract = "Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. At the same time, Dual Encoder (DE) based retrieval systems project queries and documents into the same embedding space and have demonstrated their success in retrieval and bi-text mining. To match speech and text in many languages, we propose using LLMs to initialize multi-modal DE retrieval systems. Unlike traditional methods, our system doesn{'}t require speech data during LLM pre-training and can exploit LLM{'}s multilingual text understanding capabilities to match speech and text in languages unseen during retrieval training. Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages. Our system outperforms previous systems trained explicitly on all 102 languages. We achieve a 10{\%} absolute improvement in Recall@1 averaged across these languages. Additionally, our model demonstrates cross-lingual speech and text matching, which is further enhanced by readily available machine translation data.",
}
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<abstract>Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. At the same time, Dual Encoder (DE) based retrieval systems project queries and documents into the same embedding space and have demonstrated their success in retrieval and bi-text mining. To match speech and text in many languages, we propose using LLMs to initialize multi-modal DE retrieval systems. Unlike traditional methods, our system doesn’t require speech data during LLM pre-training and can exploit LLM’s multilingual text understanding capabilities to match speech and text in languages unseen during retrieval training. Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages. Our system outperforms previous systems trained explicitly on all 102 languages. We achieve a 10% absolute improvement in Recall@1 averaged across these languages. Additionally, our model demonstrates cross-lingual speech and text matching, which is further enhanced by readily available machine translation data.</abstract>
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%0 Conference Proceedings
%T Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems
%A Palma Gomez, Frank
%A Sanabria, Ramon
%A Sung, Yun-hsuan
%A Cer, Daniel
%A Dalmia, Siddharth
%A Hernandez Abrego, Gustavo
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand (in-person and online)
%F palma-gomez-etal-2024-transforming
%X Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. At the same time, Dual Encoder (DE) based retrieval systems project queries and documents into the same embedding space and have demonstrated their success in retrieval and bi-text mining. To match speech and text in many languages, we propose using LLMs to initialize multi-modal DE retrieval systems. Unlike traditional methods, our system doesn’t require speech data during LLM pre-training and can exploit LLM’s multilingual text understanding capabilities to match speech and text in languages unseen during retrieval training. Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages. Our system outperforms previous systems trained explicitly on all 102 languages. We achieve a 10% absolute improvement in Recall@1 averaged across these languages. Additionally, our model demonstrates cross-lingual speech and text matching, which is further enhanced by readily available machine translation data.
%U https://aclanthology.org/2024.iwslt-1.4
%P 23-32
Markdown (Informal)
[Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems](https://aclanthology.org/2024.iwslt-1.4) (Palma Gomez et al., IWSLT 2024)
ACL
- Frank Palma Gomez, Ramon Sanabria, Yun-hsuan Sung, Daniel Cer, Siddharth Dalmia, and Gustavo Hernandez Abrego. 2024. Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pages 23–32, Bangkok, Thailand (in-person and online). Association for Computational Linguistics.