然而transformer在前向传递(forward pass)中对每个token消耗相同数量的计算资源。理想情况下,transformer应通过不必要地消耗计算资源来使用更小的总计算预算。 条件计算(Conditional computation)是一种试图通过仅在需要时消耗计算资源来减少总计算量的技术。不同的算法提供了何时以及应使用多少计算资源的解决方案。然而,这个...
When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual infor...
该研究以「Shared functional specialization in transformer-based language models and the human brain」为题于 2024 年 6 月 29 日发布在《Nature Communications》。语言理解从根本上来说是一个建设性的过程。我们的大脑解决词语之间的局部依赖关系，将低级语言单位组装成高级意义单位，最终形成我们用来理解世界的叙述。
《Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping》阅读笔记 窥探 nlp算法工程师 6 人赞同了该文章 背景 基于transformer架构的预训练语言模型展现出了明显的优越性,刷新了众多nlp下游任务的效果。但是预训练语言模型是从海量无监督数据集中学习知识,且模型规模一般都比较大(ba...
In order to address the issue of polysemy in the Sesotho sa Leboa language, this study set out to create a word sense discrimination (WSD) scheme using a corpus-based hybrid transformer-based architecture and deep learning models. Additionally, the performance of baseline and impr...
1.2 自回归语言模型 (Autoregressive Language Models)大多数LLMs以「自回归方式」(Autoregressive)操作,这意味着它们根据前面的「文本」预测下一个「字」(或token/sub-word)的「概率分布」(propability distribution)。这种自回归特性使模...
The surge of pre-trained language models has begun a new era in the field of Natural Language Processing (NLP) by allowing us to build powerful language models. Among these models, Transformer-based models such as BERT have become increasingly popular due to their state-of-the-art performance...
Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0). - jalammar/ecco
Encoder-decoder masked language models (MLMs), such as BERT and its many derivatives, represent the other main evolutionary branch of transformer-based LLMs. In training, an MLM is provided a text sample with some tokensmasked—hidden—and tasked with completing the missing information. ...
in optimizing these models for smaller datasets and using transfer learning to solve new problems. This allows certain activities to be performed more effectively while using fewer data. Various parameters of the transformer-based language model are shown in Figure 1....