CPM: A Large-scale Generative Chinese Pre-trained Language Model
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Abstract (arXiv)
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 of attention due to the capacity of few-shot (even zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many NLP tasks in the settings of few-shot (even zero-shot) learning. The code and parameters are available at https://github.com/TsinghuaAI/CPM-Generate.
Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2020.
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Citing papers per year: 2021: 19; 2022: 21; 2023: 9; 2024: 7; 2025: 4.
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- 2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generationcited by 258
- 2022Black-Box Tuning for Language-Model-as-a-Servicecited by 254
- 2022Compute Trends Across Three Eras of Machine Learningcited by 161
- 2021PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computationcited by 134
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- 2021A Survey on Gender Bias in Natural Language Processingcited by 89
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- 2024The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling11 Oct 2024
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- 2024Modeling Comparative Logical Relation with Contrastive Learning for Text Generation13 Jun 2024
- 2024Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback30 May 2024
- 2024Pretraining and Updates of Domain-Specific LLM: A Case Study in the Japanese Business Domain12 Apr 2024
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