Figure 2. Image shows the structure of encoder-decoder language models. There are several classes of large language models that are suited for different types of use cases: Encoder only: These models are typically suited for tasks that can understand language, such as classification and sentiment ...
5. Multimodal science question 5.1. ScienceQA ScienceQA is the first large-scale multimodal science question dataset that annotates the answers with detailed lectures and explanations. Question classes: NAT = natural science, SOC = social science, LAN = language science, TXT = text context, IMG ...
Discover the leading large language models examples with insights on business adoption, language model training, and influential models.
Using datasets from middle school mathematics and Chinese classes, classroom dialogues were manually coded by experts and then analysed with a customised GPT-4 model. The study compares manual annotations with GPT-4 outputs to evaluate efficacy. Metrics include time efficiency, inter...
output space(classes or answer choices)比较重要,例如是分类 “positive/neutral/negative”,还是分类 “tech/sports/finance” 优点 一个模型解决无数问题:GPT3 175B 做 in-context learning 性能约等于T5-large 770M 全数据 finetune 缺点 模型对不同的 context 较为敏感,例如几个例子的顺序 由于context size...
large language models50,51. Beyond trees, there are many popular classes of rule-based models, such as rule sets52, rule lists53,54, and tree sums14. Aug-Tree addresses a common problem shared by rule-based approaches: modeling the sparse, correlated features that are common in tasks such...
On the Application of Task-based Language Teaching in Junior High School任务型教学法在初中英语的应用 On Application of Task-based Approach to Reading Teaching in English Classes 任务型教学在英语课堂阅读教学中的应用 交际式教学法与任务型教学法在中国英语课堂的发展 on the development of clt and tblt...
This blog post introduced the idea that a large language model maintains a set of simulated characters in superposition. Wei, J. et al. Emergent abilities of large language models. Trans. Mach. Learn. Res. https://openreview.net/forum?id=yzkSU5zdwD (2022). Vaswani, A. et al. Attention...
Finally, three vectors u, v and |u-v| are concatenated, multiplied by a trainable weight matrix W and the multiplication result is fed into the softmax classifier which outputs normalised probabilities of sentences corresponding to different classes. The cross-entropy loss function is used to upda...
(previouslyAWS PyTorch S3 Plugin) andWebdataset. The AWS Python SDKBoto3may also be combined withTorch Datasetclasses to create custom data loading code. Custom data loading classes also enable the creative use of SageMaker Training heterogeneous clusters, to finely adapt the CPU and GPU ba...