23 citations · 41 across the 9 of their papers we have counts for
9 papers
Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer
Jinghui Yuan, Jiaxuan Zou, Shuo Wang +2
Matrix-based optimizers have demonstrated immense potential in training Large Language Models (LLMs), however, designing an ideal optimizer remains a formidable challenge. A superi…
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages
Yuanchi Zhang, Yile Wang, Zijun Liu +5
While large language models (LLMs) have been pre-trained on multilingual corpora, their performance still lags behind in most languages compared to a few resource-rich languages. O…
Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
Yuzhuang Xu, Shuo Wang, Peng Li +4
Communication games, which we refer to as incomplete information games that heavily depend on natural language communication, hold significant research value in fields such as econ…
A Template-based Method for Constrained Neural Machine Translation
Shuo Wang, Peng Li, Zhixing Tan +3
Machine translation systems are expected to cope with various types of constraints in many practical scenarios. While neural machine translation (NMT) has achieved strong performan…
Understanding and Mitigating the Uncertainty in Zero-Shot Translation
Wenxuan Wang, Wenxiang Jiao, Shuo Wang +2
Zero-shot translation is a promising direction for building a comprehensive multilingual neural machine translation~(MNMT) system. However, its quality is still not satisfactory du…
Integrating Vectorized Lexical Constraints for Neural Machine Translation
Shuo Wang, Zhixing Tan, Yang Liu
Lexically constrained neural machine translation (NMT), which controls the generation of NMT models with pre-specified constraints, is important in many practical scenarios. Due to…
On the Language Coverage Bias for Neural Machine Translation
Shuo Wang, Zhaopeng Tu, Zhixing Tan +3
Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine t…
On the Inference Calibration of Neural Machine Translation
Shuo Wang, Zhaopeng Tu, Shuming Shi +1
Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer u…
Improving Back-Translation with Uncertainty-based Confidence Estimation
Shuo Wang, Yang Liu, Chao Wang +2
While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora…