3 citations · 9 across the 11 of their papers we have counts for
12 papers
DiffScore: Text Evaluation Beyond Autoregressive Likelihood
Wen Lai, Yingli Shen, Dingnan Jin +4
Autoregressive language models are widely used for text evaluation, however, their left-to-right factorization introduces positional bias, i.e., early tokens are scored with only l…
FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding
Yingli Shen, Wen Lai, Jie Zhou +7
Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages. Existing resources impose a tr…
From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora
Yingli Shen, Wen Lai, Shuo Wang +4
Continued pretraining and instruction tuning on large-scale multilingual data have proven to be effective in scaling large language models (LLMs) to low-resource languages. However…
DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly Detection
Yingli Shen, Wen Lai, Shuo Wang +4
The rapid development of multilingual large language models (LLMs) highlights the need for high-quality, diverse, and well-curated multilingual datasets. In this paper, we introduc…
Joint Localization and Activation Editing for Low-Resource Fine-Tuning
Wen Lai, Alexander Fraser, Ivan Titov
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, are commonly used to adapt LLMs. However, the effectiveness of standard PEFT methods is limited in low-resource scenar…
Style-Specific Neurons for Steering LLMs in Text Style Transfer
Wen Lai, Viktor Hangya, Alexander Fraser
Text style transfer (TST) aims to modify the style of a text without altering its original meaning. Large language models (LLMs) demonstrate superior performance across multiple ta…
LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback
Wen Lai, Mohsen Mesgar, Alexander Fraser
To democratize large language models (LLMs) to most natural languages, it is imperative to make these models capable of understanding and generating texts in many languages, in par…
Vision-Language Model-based Physical Reasoning for Robot Liquid Perception
Wenqiang Lai, Yuan Gao, Tin Lun Lam
There is a growing interest in applying large language models (LLMs) in robotic tasks, due to their remarkable reasoning ability and extensive knowledge learned from vast training…
Extending Multilingual Machine Translation through Imitation Learning
Wen Lai, Viktor Hangya, Yingli Shen +1
Despite the growing variety of languages supported by existing multilingual neural machine translation (MNMT) models, most of the world's languages are still being left behind. We…
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation
Wen Lai, Alexandra Chronopoulou, Alexander Fraser
Despite advances in multilingual neural machine translation (MNMT), we argue that there are still two major challenges in this area: data imbalance and representation degeneration.…
: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter
Wen Lai, Alexandra Chronopoulou, Alexander Fraser
Multilingual neural machine translation models (MNMT) yield state-of-the-art performance when evaluated on data from a domain and language pair seen at training time. However, when…
Improving Both Domain Robustness and Domain Adaptability in Machine Translation
Wen Lai, Jindřich Libovický, Alexander Fraser
We consider two problems of NMT domain adaptation using meta-learning. First, we want to reach domain robustness, i.e., we want to reach high quality on both domains seen in the tr…