Summary Retentive Network A Successor to Transformer arxiv.org
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The Retentive Network (RetNet) is a proposed successor to the Transformer model that introduces a retention mechanism to achieve training parallelism, low-cost inference, and good performance for large language models.
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Slide Presentation (8 slides)
Key Points
- The Retentive Network (RetNet) is proposed as a successor to the Transformer for large language models.
- RetNet aims to achieve training parallelism, low-cost inference, and good performance.
- RetNet introduces a retention mechanism that can be represented in three different forms: parallel representation, recurrent representation, and chunkwise recurrent representation.
- RetNet achieves better inference efficiency, training parallelization, and competitive performance compared to Transformers.
- RetNet enables various representations, including parallel, recurrent, and chunkwise.
Summaries
32 word summary
The Retentive Network (RetNet) is a proposed successor to the Transformer model for large language models. It aims to achieve training parallelism, low-cost inference, and good performance by introducing a retention mechanism.
38 word summary
The Retentive Network (RetNet) is proposed as a successor to the Transformer model for large language models. It aims to achieve training parallelism, low-cost inference, and good performance. RetNet introduces a retention mechanism that can be represented in
343 word summary
The Retentive Network (RetNet) is proposed as a successor to the Transformer for large language models. It aims to achieve training parallelism, low-cost inference, and good performance. The connection between recurrence and attention is theoretically derived, and a
Retentive Network (RetNet) is a successor to Transformer that offers efficient memory and computation inferences. It simplifies implementation without key-value cache tricks and allows for efficient long-sequence modeling. Experimental results show that RetNet is competitive in scaling
The document discusses the Retentive Network (RetNet), which is a successor to the Transformer model. RetNet introduces a retention mechanism that can be represented in three different forms: parallel representation, recurrent representation, and chunkwise recurrent representation. The parallel
The Retentive Network (RetNet) is a successor to the Transformer model. It introduces several modifications to stabilize numerical flow and improve performance. The overall architecture of RetNet consists of multi-scale retention (MSR) and feed-forward network (FF
The document discusses the Retentive Network (RetNet), a successor to the Transformer model. The text provides information on the sizes and learning hyper-parameters of the models used in language modeling experiments. It shows that RetNet tends to outperform Transformer
We conducted evaluations of zero-shot and 4-shot learning with our 6.7B models on various datasets. Our Retentive Network (RetNet) achieved comparable performance to Transformer on zero-shot and in-context learning settings. We compared the training
Retentive Networks (RetNet) are proposed as a successor to Transformers for sequence modeling. RetNet achieves better inference efficiency, training parallelization, and competitive performance compared to Transformers. It enables various representations, including parallel, recurrent, and chunkwise
This document provides a list of references to various papers and articles related to the topic of language modeling and neural networks. The references cover a range of subjects including representation collapse, attention mechanisms, long document summarization, language models as interfaces, autoregressive
In this document, several research papers are cited, including ones on the topic of transforming language models. The document also includes a table of hyperparameters used for the models discussed in Section 3. It mentions the use of different context lengths and compares the