29 citations · 86 across the 9 of their papers we have counts for
11 papers
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization
Yuxian Gu, Pei Ke, Xiaoyan Zhu +1
Training language models to learn from human instructions for zero-shot cross-task generalization has attracted much attention in NLP communities. Recently, instruction tuning (IT)…
Rethinking and Refining the Distinct Metric
Siyang Liu, Sahand Sabour, Yinhe Zheng +3
Distinct- score\cite{Li2016} is a widely used automatic metric for evaluating diversity in language generation tasks. However, we observed that the original approach for calcula…
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Hao Zhou, Pei Ke, Zheng Zhang +11
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue dat…
CPM-2: Large-scale Cost-effective Pre-trained Language Models
Zhengyan Zhang, Yuxian Gu, Xu Han +16
In recent years, the size of pre-trained language models (PLMs) has grown by leaps and bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real-…
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Pei Ke, Haozhe Ji, Yu Ran +5
Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which la…
CPM: A Large-scale Generative Chinese Pre-trained Language Model
Zhengyan Zhang, Xu Han, Hao Zhou +22
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot o…