参考论文《CPM-2: Large-scale Cost-effective Pre-trained Language Models》 针对预训练语言模型(PLM)问题限制了它们在现实世界场景中的使⽤,作者提出了⼀套使⽤PLM来处理预训练、微调和推理的效率问题的具有成本效益的技术,该技术主要分成3个方面: (1) 引⼊知识继承,通过利⽤现有的PLM⽽不是从头开始...
Artificial writing is permeating our lives due to recent advances in large-scale, transformer-based language models (LMs) such as BERT, GPT-2 and GPT-3. Using them as pre-trained models and fine-tuning them for specific tasks, researchers have extended t
这篇论文来自《PANGU-α: LARGE-SCALE AUTOREGRESSIVE PRETRAINED CHINESE LANGUAGE MODELS WITH AUTO-PARALLEL COMPUTATION》 摘要 ⼤规模预训练语⾔模型 (PLM) 已成为⾃然语⾔处理 (NLP) 的新范例。具有数千亿个参数的PLM在⾃然语⾔理解和⽣成⽅⾯表现出强⼤的性能,并带有少样本上下⽂学习。在这...
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 ...
However, how much pre-trained language models can help dialog response generation is still under exploration. In this paper, we propose a simple, general, and effective framework: Alternating Recurrent Dialog Model (ARDM). ARDM models each speaker separately and takes advantage of large pre-trained...
Hence, how to pre-train a large-scale Chinese language model needs more exploration, such as the construction of Chinese vocabulary and the design of the training strategy. In this technical report, we release the Chinese Pre-trained Language Model (CPM) with generative pre-training on large-...
rain. So far, there has been no large-scale application of products based on large models in the industry, and the reasons behind it still need to be further explored. What is the capability boundary of a large model that only uses general corpus and is not pre-trained with industry data...
With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all the parameters is prohibitively costly and eventually be...
We pre-trained our models on Philly (a Microsoft internal compute cluster), the code is specialized for multi-node multi-GPU compute on this platform. The pre-training main python isrun_lm_vae_pretraining_phdist_beta.py. You may need to adjust the distributed training scripts. ...
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 fewshot (even zero-shot) learning. However, applying GPT-3 to address Chin...