13 citations · 18 across the 6 of their papers we have counts for
6 papers
An Empirical Study of Many-to-Many Summarization with Large Language Models
Jiaan Wang, Fandong Meng, Zengkui Sun +5
Many-to-many summarization (M2MS) aims to process documents in any language and generate the corresponding summaries also in any language. Recently, large language models (LLMs) ha…
Warmup-Distill: Bridge the Distribution Mismatch between Teacher and Student before Knowledge Distillation
Zengkui Sun, Yijin Liu, Fandong Meng +3
The widespread deployment of Large Language Models (LLMs) is hindered by the high computational demands, making knowledge distillation (KD) crucial for developing compact smaller o…
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation
Zengkui Sun, Yijin Liu, Fandong Meng +3
Multilingual neural machine translation models generally distinguish translation directions by the language tag (LT) in front of the source or target sentences. However, current LT…
Dual-Space Knowledge Distillation for Large Language Models
Songming Zhang, Xue Zhang, Zengkui Sun +2
Knowledge distillation (KD) is known as a promising solution to compress large language models (LLMs) via transferring their knowledge to smaller models. During this process, white…
Cross-Lingual Knowledge Editing in Large Language Models
Jiaan Wang, Yunlong Liang, Zengkui Sun +3
Knowledge editing aims to change language models' performance on several special cases (i.e., editing scope) by infusing the corresponding expected knowledge into them. With the re…
Is ChatGPT a Good NLG Evaluator? A Preliminary Study
Jiaan Wang, Yunlong Liang, Fandong Meng +6
Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. Many prior studies have shown that ChatGPT achieves remarkable perform…