深度学习论文分享（四）Retentive Network: A Successor to Transformer for Large Language Models - 代码天地

深度学习论文分享（四）Retentive Network: A Successor to Transformer for Large Language Models

编程语言 2023-07-29 01:47:07 阅读次数: 0

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转载自blog.csdn.net/qq_52358603/article/details/131900911

深度学习论文分享（四）Retentive Network: A Successor to Transformer for Large Language Models

Arxiv 2307 | Retentive Network: A Successor to Transformer for Large Language Models

【论文精读】Emergent Abilities of Large Language Models

论文阅读 A Survey of Large Language Models 3

论文阅读 A Survey of Large Language Models 2

论文阅读 A Survey of Large Language Models 1

论文解读：Large Language Models as Analogical Reasoners

A Survey on Multimodal Large Language Models论文解读

Are Large Language Models Chameleons?

Unraveling the Dominance of Large Language Models Over Transformer Models for Bangla Natural Languag

Challenges and Applications of Large Language Models

Large Language Models in Finance: A Survey

A Survey of Large Language Models Attribution

Artificial Agency and Large Language Models

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

论文阅读：chain of thought Prompting elicits reasoning in large language models

论文阅读-Transformer-based language models for software vulnerability detection

Are Emergent Abilities of Large Language Models a Mirage?

Enabling Large Language Models to Generate Text with Citations

A Survey on Model Compression for Large Language Models

Recommender Systems in the Era of Large Language Models (LLMs)

Augmented Large Language Models with Parametric Knowledge Guiding

ADELIE: Aligning Large Language Models on Information Extraction

RETRIEVAL MEETS LONG CONTEXT LARGE LANGUAGE MODELS

TASKBENCH: BENCHMARKING LARGE LANGUAGE MODELS FOR TASK AUTOMATION

A Survey on Multimodal Large Language Models for Autonomous Driving

Trends in Integration of Knowledge and Large Language Models

A Survey of Text Watermarking in the Era of Large Language Models

Large Language Models in Targeted Sentiment Analysis for Russian

Empowering Large Language Models for Textual Data Augmentation

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