@inproceedings{vashee-2024-evolving,
title = "The Evolving Path to {LLM}-based {MT}",
author = "Vashee, Kirti",
editor = "Martindale, Marianna and
Campbell, Janice and
Savenkov, Konstantin and
Goel, Shivali",
booktitle = "Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations)",
month = sep,
year = "2024",
address = "Chicago, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2024.amta-presentations.2",
pages = "18--18",
abstract = "This session will explore the challenges and obstacles we face in transitioning from current SOTA NMT models to an LLM-based MT landscape for enterprise use cases. NMT models are now pervasive and utilized in many production scenarios from eCommerce, eDiscovery, and Customer Service {\&} Support. While LLM MT shows promise with high-resource language translation there are significant latency, throughput, and adaptation challenges to resolve. The session will look at key questions like: Can LLM MT scale to the same levels as current NMT technology? What innovation can we expect from LLM MT to further the SOTA? What other impact will GenAI have on localization production practices? Will there be an interim hybrid period where both NMT and GenAI work together in production workflows? Will LLM MT be able to address low-resource language requirements? How will multilingual LLMs being developed across the world affect the Big Tech and English-centric dominance we see in GenAI today?",
}
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%0 Conference Proceedings
%T The Evolving Path to LLM-based MT
%A Vashee, Kirti
%Y Martindale, Marianna
%Y Campbell, Janice
%Y Savenkov, Konstantin
%Y Goel, Shivali
%S Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations)
%D 2024
%8 September
%I Association for Machine Translation in the Americas
%C Chicago, USA
%F vashee-2024-evolving
%X This session will explore the challenges and obstacles we face in transitioning from current SOTA NMT models to an LLM-based MT landscape for enterprise use cases. NMT models are now pervasive and utilized in many production scenarios from eCommerce, eDiscovery, and Customer Service & Support. While LLM MT shows promise with high-resource language translation there are significant latency, throughput, and adaptation challenges to resolve. The session will look at key questions like: Can LLM MT scale to the same levels as current NMT technology? What innovation can we expect from LLM MT to further the SOTA? What other impact will GenAI have on localization production practices? Will there be an interim hybrid period where both NMT and GenAI work together in production workflows? Will LLM MT be able to address low-resource language requirements? How will multilingual LLMs being developed across the world affect the Big Tech and English-centric dominance we see in GenAI today?
%U https://aclanthology.org/2024.amta-presentations.2
%P 18-18
Markdown (Informal)
[The Evolving Path to LLM-based MT](https://aclanthology.org/2024.amta-presentations.2) (Vashee, AMTA 2024)
ACL
- Kirti Vashee. 2024. The Evolving Path to LLM-based MT. In Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations), pages 18–18, Chicago, USA. Association for Machine Translation in the Americas.