Neural Machine Translation Models Can Learn to be Few-shot Learners

15 September 2023

Papers citing "Neural Machine Translation Models Can Learn to be Few-shot Learners"

6 / 6 papers shown

Title
Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers Libo Qin Qiguang Chen Yuhang Zhou Zhi Chen Hai-Tao Zheng Lizi Liao Min Li Wanxiang Che Philip S. Yu LRM 55 36 0 07 Apr 2024
Fine-tuning Large Language Models for Domain-specific Machine Translation Jiawei Zheng Hanghai Hong Xiaoli Wang Jingsong Su Yonggui Liang Shikai Wu ALM 52 32 0 23 Feb 2024
Rethinking Human-like Translation Strategy: Integrating Drift-Diffusion Model with Large Language Models for Machine Translation Hongbin Na Zimu Wang M. Maimaiti Tong Chen Wei Wang Tao Shen Ling Chen LRM 25 5 0 16 Feb 2024
Fine-tuning Large Language Models for Adaptive Machine Translation Yasmin Moslem Rejwanul Haque Andy Way 23 25 0 20 Dec 2023
Assessing Translation capabilities of Large Language Models involving English and Indian Languages Vandan Mujadia Ashok Urlana Yash Bhaskar Penumalla Aditya Pavani Kukkapalli Shravya Parameswari Krishnamurthy D. Sharma ELM 151 7 0 15 Nov 2023
NeMo: a toolkit for building AI applications using Neural Modules Oleksii Kuchaiev Jason Chun Lok Li Huyen Nguyen Oleksii Hrinchuk Ryan Leary ... Jack Cook P. Castonguay Mariya Popova Jocelyn Huang Jonathan M. Cohen 211 292 0 14 Sep 2019