GLaM: Efficient Scaling of Language Models with Mixture-of …
2021年12月13日 · In this paper, we propose and develop a family of language models named GLaM (Generalist Language Model), which uses a sparsely activated mixture-of-experts …
Graph-Aware Language Model Pre-Training on a Large Graph …
2023年6月5日 · To address this problem, we propose a framework of graph-aware language model pre-training (GALM) on a large graph corpus, which incorporates large language …
Google 发布GLaM：万亿权重语言学习模型来更好地理解上下文信 …
GLaM 性能优于密集语言模型 GPT-3 (175B)，在七个类别的 29 个公共 NLP 基准测试中显着提高了学习效率，涵盖语言完成、开放域问答和自然语言推理任务。 为了构建 GLaM，Google首 …
GPT-3被超越？解读低能耗、高性能的GlaM模型 - 知乎
在这篇论文中，作者开发了以Mixture of Experts为基础的GlaM (Generalist Language Model)。它虽然参数量有GPT-3的7倍之多，但训练起来只需GPT-3三分之一的能耗，而且在NLP任务的 …
MOE论文详解(4)-GLaM - CSDN博客
2024年10月18日 · 2022年google在`GShard`之后发表另一篇跟MoE相关的paper, 论文名为`GLaM (Generalist Language Model)`, 最大的GLaM模型有1.2 trillion参数, 比GPT-3大7倍, 但成本只 …
More Efficient In-Context Learning with GLaM - Google Research
Our large-scale sparsely activated language model, GLaM, achieves competitive results on zero-shot and one-shot learning and is a more efficient model than prior monolithic dense …
To address this problem, we propose a framework of graph-aware language model pre-training (GaLM) on a large graph corpus, which incor-porates large language models and graph neural …
In this paper, we propose and develop a family of language mod-els named GLaM (Generalist Language Model), which uses a sparsely activated mixture-of-experts architecture to scale the …
GLaM: Fine-Tuning Large Language Models for Domain …
2024年5月20日 · We introduce a fine-tuning framework for developing Graph-aligned Language Models (GaLM) that transforms a knowledge graph into an alternate text representation with …
1.2万亿参数：谷歌通用稀疏语言模型GLaM，小样本学习打败GPT …
为了回答这个问题，谷歌推出了具有万亿权重的通用语言模型 (Generalist Language Model，GLaM)，该模型的一大特点就是具有稀疏性，可以高效地进行训练和服务（在计算和 …