4 citations · 7 across the 7 of their papers we have counts for
7 papers · 1 filter
Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention
Weixuan Wang, Minghao Wu, Barry Haddow +1
Large Language Models (LLMs) have shown remarkable capabilities in natural language processing but exhibit significant performance gaps among different languages. Most existing app…
Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors
Weixuan Wang, Jingyuan Yang, Wei Peng
Large language models (LLMs) have achieved remarkable performance across many tasks, yet aligning them with desired behaviors remains challenging. Activation intervention has emerg…
Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs
Weixuan Wang, Barry Haddow, Minghao Wu +2
Large language models (LLMs) have revolutionized the field of natural language processing (NLP), and recent studies have aimed to understand their underlying mechanisms. However, m…
Retrieval-augmented Multilingual Knowledge Editing
Weixuan Wang, Barry Haddow, Alexandra Birch
Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time. Updating knowledge via fine-tuning is computationally resourc…
Evaluating the Efficacy of Length-Controllable Machine Translation
Hao Cheng, Meng Zhang, Weixuan Wang +3
Length-controllable machine translation is a type of constrained translation. It aims to contain the original meaning as much as possible while controlling the length of the transl…
Learning Homographic Disambiguation Representation for Neural Machine Translation
Weixuan Wang, Wei Peng, Qun Liu
Homographs, words with the same spelling but different meanings, remain challenging in Neural Machine Translation (NMT). While recent works leverage various word embedding approach…