8 citations · 13 across the 7 of their papers we have counts for
7 papers
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention
Changjiang Gao, Shujian Huang, Jixing Li +1
Recent large language models (LLMs) have revealed strong abilities to understand natural language. Since most of them share the same basic structure, i.e. the transformer block, po…
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
Xu Huang, Zhirui Zhang, Ruize Gao +6
We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art model…
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation
Zihan Liu, Zewei Sun, Shanbo Cheng +2
Document-level Neural Machine Translation (DocNMT) has been proven crucial for handling discourse phenomena by introducing document-level context information. One of the most impor…
Food-500 Cap: A Fine-Grained Food Caption Benchmark for Evaluating Vision-Language Models
Zheng Ma, Mianzhi Pan, Wenhan Wu +4
Vision-language models (VLMs) have shown impressive performance in substantial downstream multi-modal tasks. However, only comparing the fine-tuned performance on downstream tasks…
Extrapolating Large Language Models to Non-English by Aligning Languages
Wenhao Zhu, Yunzhe Lv, Qingxiu Dong +6
Existing large language models show disparate capability across different languages, due to the imbalance in the training data. Their performances on English tasks are often strong…
INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation
Wenhao Zhu, Jingjing Xu, Shujian Huang +2
Neural machine translation has achieved promising results on many translation tasks. However, previous studies have shown that neural models induce a non-smooth representation spac…