Investigating the translation capabilities of Large Language Models trained on parallel data only
Authors: Javier García Gilabert, Carlos Escolano, Aleix Sant, Francesca De Luca Fornaciari, Audrey Mash, Xixian Liao, Maite Melero


Reviewer paper summaries: This paper introduced several language models trained from scratch solely by using parallel datasets and archived comparable performance with existing large language models. Further analyses show that different language models focus on different parts of prompts, they reduced around half of attention heads with only 2 BLEU scores sacrificed.
Summary of strengths: The idea is solid and the writing is easy to understand.
Summary of weaknesses: I am not sure if I understand your paper correctly, please correct me if I missed anything. My concern is that why you put these two things in the same paper, your parallel data training part and the attention head reduction parts don't seem related to each other.

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Update:

According to the author's response, it sounds like these two contributions are just not related, and there is no reason to put them in the same paper, so I changed my score.
Comments, suggestions and typos: n/a
Overall assessment scores: 3