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    <abstract>Embed_llama is an assessment metric for language translation that hinges upon the utilization of the recently introduced Llama 2 Large Language Model (LLM), specifically, focusing on its embedding layer, with the aim of transforming sentences into a vector space that establishes connections between geometric and semantic proximities</abstract>
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%0 Conference Proceedings
%T Embed_Llama: Using LLM Embeddings for the Metrics Shared Task
%A Dreano, Sören
%A Molloy, Derek
%A Murphy, Noel
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F dreano-etal-2023-embed
%X Embed_llama is an assessment metric for language translation that hinges upon the utilization of the recently introduced Llama 2 Large Language Model (LLM), specifically, focusing on its embedding layer, with the aim of transforming sentences into a vector space that establishes connections between geometric and semantic proximities
%R 10.18653/v1/2023.wmt-1.60
%U https://aclanthology.org/2023.wmt-1.60
%U https://doi.org/10.18653/v1/2023.wmt-1.60
%P 738-745

Markdown (Informal)

[Embed_Llama: Using LLM Embeddings for the Metrics Shared Task](https://aclanthology.org/2023.wmt-1.60) (Dreano et al., WMT 2023)

• Embed_Llama: Using LLM Embeddings for the Metrics Shared Task (Dreano et al., WMT 2023)

ACL

• Sören Dreano, Derek Molloy, and Noel Murphy. 2023. Embed_Llama: Using LLM Embeddings for the Metrics Shared Task. In Proceedings of the Eighth Conference on Machine Translation, pages 738–745, Singapore. Association for Computational Linguistics.