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- Anthology ID:
- 2023.wmt-1.60
- Volume:
- Proceedings of the Eighth Conference on Machine Translation
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 738–745
- Language:
- URL:
- https://aclanthology.org/2023.wmt-1.60
- DOI:
- 10.18653/v1/2023.wmt-1.60
- Cite (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.
- Cite (Informal):
- Embed_Llama: Using LLM Embeddings for the Metrics Shared Task (Dreano et al., WMT 2023)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2023.wmt-1.60.pdf