58 citations · 71 across the 13 of their papers we have counts for
11 papers
LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning
Tao Fang, Derek F. Wong, Lusheng Zhang +5
While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to…
Learning from "Silly" Questions Improves Large Language Models, But Only Slightly
Tingyuan Zhu, Shudong Liu, Yidong Wang +4
Constructing high-quality Supervised Fine-Tuning (SFT) datasets is critical for the training of large language models (LLMs). Recent studies have shown that using data from a speci…
AnyTrans: Translate AnyText in the Image with Large Scale Models
Zhipeng Qian, Pei Zhang, Baosong Yang +5
This paper introduces AnyTrans, an all-encompassing framework for the task-Translate AnyText in the Image (TATI), which includes multilingual text translation and text fusion withi…
What is the Best Way for ChatGPT to Translate Poetry?
Shanshan Wang, Derek F. Wong, Jingming Yao +1
Machine translation (MT) has historically faced significant challenges when applied to literary works, particularly in the domain of poetry translation. The advent of Large Languag…
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
Xinyu Ma, Xuebo Liu, Derek F. Wong +6
Multimodal machine translation (MMT) is a challenging task that seeks to improve translation quality by incorporating visual information. However, recent studies have indicated tha…
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model
Runzhe Zhan, Xinyi Yang, Derek F. Wong +2
While supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language model (LLM) to specific preferences, concerns have been…