Summary Reducing Parameters in Transformer Architecture for Improved Efficiency arxiv.org
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The paper focuses on enhancing efficiency in the Transformer architecture by reducing parameters, specifically in the Feed Forward Network (FFN), and evaluates the impact of removing the FFN through experimental investigation.
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Key Points
- The authors of this paper explore the role of the Feed Forward Network (FFN) in the Transformer architecture and find that it is highly redundant despite its significant parameter usage.
- The authors aim to improve the efficiency of the Transformer architecture by reducing the number of parameters, particularly in the feed-forward networks (FFNs) of the encoder and decoder.
- The Local Neighborhood Similarity (LNS) method is used to measure the similarity between the semantic spaces of different models in natural language processing.
- The authors conducted experiments to reduce the number of parameters in the Transformer architecture while improving efficiency, finding that the encoder and decoder FFNs have different contributions and sharing one FFN on the encoder can lead to improvements.
- Sharing feed-forward networks (FFNs) in the Transformer architecture consistently lowers similarity scores and decreases redundancy within the network.
- Different models and configurations were experimented with to analyze their impact on accuracy and inference speed, with dropping the decoder FFNs in the Deep Encoder Shallow Decoder model resulting in improvements.
- Strategies for reducing the number of parameters in neural machine translation were explored, including different ways of sharing feed-forward networks (FFNs) within a module of N layers.
Summaries
32 word summary
This paper aims to improve efficiency in the Transformer architecture by reducing parameters, particularly in the Feed Forward Network (FFN). Experimental investigation is conducted to assess the effects of removing the FFN.
45 word summary
The authors of this paper aim to improve the efficiency of the Transformer architecture by reducing the number of parameters. They focus on the redundancy of the Feed Forward Network (FFN) and conduct experiments to determine the impact of removing it. The Local Neighborhood Similar
479 word summary
The authors of this paper explore the role of the Feed Forward Network (FFN) in the Transformer architecture and find that it is highly redundant despite its significant parameter usage. They conduct experiments and find that they can substantially reduce the number of parameters by removing
The authors of this study aim to improve the efficiency of the Transformer architecture by reducing the number of parameters. They focus on the feed-forward networks (FFNs) in the encoder and decoder, which make up the majority of the parameter budget. Previous work
The Local Neighborhood Similarity (LNS) method is used to measure the similarity between the semantic spaces of different models. LNS determines similarity based on the similarity of sentence neighbors in the two spaces. The LNS of a sentence between two models is
In this study, the authors investigate different configurations of the transformer architecture to improve efficiency. They use dropout rates of 0.1, 0.3, and 0 for different datasets and models. The models are trained using fp16. The
The authors conducted experiments to reduce the number of parameters in the Transformer architecture while improving efficiency. They found that the encoder and decoder FFNs have different contributions, with the decoder's being more redundant. By sharing one FFN on the encoder and dropping it
The excerpt discusses the benchmark score and normalized similarity scores for several models in the Transformer architecture. It highlights that sharing feed-forward networks (FFNs) leads to consistently lower similarity scores and decreased redundancy within the network. The One Wide FFN model shows a
The study focuses on reducing parameters in the Transformer architecture to improve efficiency. The authors experiment with different models and configurations to analyze the impact on accuracy and inference speed. They find that dropping the decoder FFNs in the Deep Encoder Shallow Decoder model results in
The article discusses a method for reducing the parameters in Transformer architecture to improve efficiency. The authors found that by sharing the feed-forward network (FFN) across all encoder layers and removing it from the decoder layers, while increasing the dimension of the encoder FF
This excerpt includes references to various papers and conferences in the field of natural language processing and machine translation. The mentioned papers cover topics such as parameter efficiency in transformer architectures, scaling laws for neural machine translation, measuring statistical dependence, deep residual learning for image recognition
This excerpt is a list of references cited in a document about reducing parameters in transformer architecture to improve efficiency. The references include papers and conference proceedings related to various topics in natural language processing and machine translation. Some of the key points highlighted in the references include
In a study on the efficiency of Transformer architecture, the authors investigate strategies for reducing the number of parameters in neural machine translation. They explore different ways of sharing feed-forward networks (FFNs) within a module of N layers, including sequence, cycle,