4 papers
Lost in Literalism: How Supervised Training Shapes Translationese in LLMs
Yafu Li, Ronghao Zhang, Zhilin Wang +5
Large language models (LLMs) have achieved remarkable success in machine translation, demonstrating impressive performance across diverse languages. However, translationese, charac…
Semformer: Transformer Language Models with Semantic Planning
Yongjing Yin, Junran Ding, Kai Song +1
Next-token prediction serves as the dominant component in current neural language models. During the training phase, the model employs teacher forcing, which predicts tokens based…
LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation
Yongjing Yin, Jiali Zeng, Yafu Li +2
The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from…
What Have We Achieved on Non-autoregressive Translation?
Yafu Li, Huajian Zhang, Jianhao Yan +2
Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate wi…