306 citations · 863 across the 45 of their papers we have counts for
7 papers · 1 filter
EPA: Easy Prompt Augmentation on Large Language Models via Multiple Sources and Multiple Targets
Hongyuan Lu, Wai Lam
Large language models (LLMs) have shown promising performance on various NLP tasks via task prompting. And their performance can be further improved by appending task demonstration…
Enhancing Grammatical Error Correction Systems with Explanations
Yuejiao Fei, Leyang Cui, Sen Yang +3
Grammatical error correction systems improve written communication by detecting and correcting language mistakes. To help language learners better understand why the GEC system mak…
mPMR: A Multilingual Pre-trained Machine Reader at Scale
Weiwen Xu, Xin Li, Wai Lam +1
We present multilingual Pre-trained Machine Reader (mPMR), a novel method for multilingual machine reading comprehension (MRC)-style pre-training. mPMR aims to guide multilingual p…
Easy-to-Hard Learning for Information Extraction
Chang Gao, Wenxuan Zhang, Wai Lam +1
Information extraction (IE) systems aim to automatically extract structured information, such as named entities, relations between entities, and events, from unstructured texts. Wh…
Progressive Translation: Improving Domain Robustness of Neural Machine Translation with Intermediate Sequences
Chaojun Wang, Yang Liu, Wai Lam
Previous studies show that intermediate supervision signals benefit various Natural Language Processing tasks. However, it is not clear whether there exist intermediate signals tha…
A Frustratingly Simple Decoding Method for Neural Text Generation
Haoran Yang, Deng Cai, Huayang Li +3
We introduce a frustratingly simple, super efficient and surprisingly effective decoding method, which we call Frustratingly Simple Decoding (FSD), for neural text generation. The…
Chain-of-Dictionary Prompting Elicits Translation in Large Language Models
Hongyuan Lu, Haoran Yang, Haoyang Huang +3
Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data. Yet, despite the fa…