49 citations · 161 across the 5 of their papers we have counts for
6 papers · 1 filter
SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark
Liang Xu, Anqi Li, Lei Zhu +7
Large language models (LLMs) have shown the potential to be integrated into human daily lives. Therefore, user preference is the most critical criterion for assessing LLMs' perform…
Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning
Shaohua Wu, Xudong Zhao, Tong Yu +8
Recent work like GPT-3 has demonstrated excellent performance of Zero-Shot and Few-Shot learning on many natural language processing (NLP) tasks by scaling up model size, dataset s…
FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
Liang Xu, Xiaojing Lu, Chenyang Yuan +8
Pretrained Language Models (PLMs) have achieved tremendous success in natural language understanding tasks. While different learning schemes -- fine-tuning, zero-shot, and few-shot…
CLUE: A Chinese Language Understanding Evaluation Benchmark
Liang Xu, Hai Hu, Xuanwei Zhang +29
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These com…
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
Liang Xu, Xuanwei Zhang, Qianqian Dong
In this paper, we introduce the Chinese corpus from CLUE organization, CLUECorpus2020, a large-scale corpus that can be used directly for self-supervised learning such as pre-train…
CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
Liang Xu, Yu tong, Qianqian Dong +7
In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese. CLUENER2020 contains…