Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers (2021.findings-emnlp)
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| Challenge: | Recent improvements in NLP tasks can be attributed to the Transformer model. |
| Approach: | They propose to use parameter-sharing methods to reduce parameter budgets in generative models by using sandwich-style parameter sharing and self-attentive embedding factorization. |
| Outcome: | The proposed model outperforms the current RNN model even with significantly fewer parameters. |
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| Challenge: | Deploying Transformer networks on resource-constrained edge devices is challenging. |
| Approach: | They propose a low-rank factorization initialized by SVD-based weight transfer and parameter sharing to compress and accelerate Transformer networks. |
| Outcome: | The proposed method achieves similar performance to the baseline Transformer with 3.8 times and 1.8 times fewer parameters and achieves 2.3 times speedup and 1.5 times speed up respectively. |
How to Dissect a Muppet: The Structure of Transformer Embedding Spaces (2022.tacl-1)
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| Challenge: | Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm . a novel decomposition of Transformer output embeddables is demonstrated . |
| Approach: | They propose to decompose Transformer output embeddings into a sum of vector factors . they show multi-head attentions and feed-forwards are not equally useful in downstream applications . |
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RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals (2025.findings-emnlp)
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| Challenge: | Transformers have shown strong performance in processing sequential data, but their parameters are larger . a novel approach to reduce the model parameters while maintaining high performance is proposed . |
| Approach: | They propose a transformer-based model that processes input repeatedly in a circular, ring-like manner. |
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HashFormers: Towards Vocabulary-independent Pre-trained Transformers (2022.emnlp-main)
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| Challenge: | Existing pre-trained language models are vocabulary-dependent, mapping by default each token to its corresponding embedding. |
| Approach: | They propose a family of vocabulary-independent pre-trained transformers that support unlimited vocabulary . they propose to map each token to its corresponding embedding by default . |
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Scale down Transformer by Grouping Features for a Lightweight Character-level Language Model (2020.coling-main)
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| Challenge: | Existing approaches to character-level language modeling have suffered from high learning complexity caused by inherently long character sequences. |
| Approach: | They propose a method that efficiently reduces the computational cost and parameter size of Transformer by splitting feature space into multiple groups, factorizing the calculation paths, and reducing computations for the group interaction. |
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Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks (2021.acl-long)
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| Challenge: | State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained language model. |
| Approach: | They propose a framework that can learn adapter parameters for all layers and tasks by generating them using shared hypernetworks. |
| Outcome: | The proposed framework improves performance on the well-known GLUE benchmark while adding only 0.29% parameters per task. |
Improving Transformer Models by Reordering their Sublayers (2020.acl-main)
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| Challenge: | a sandwich transformer pattern is a new approach to multilayer transformers that can be used for different tasks. |
| Approach: | They propose a transformer ordering pattern that reorders sublayers in a sandwich transformer pattern . they generate random transformer models and train them with the language modeling objective . |
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Understanding and Overcoming the Challenges of Efficient Transformer Quantization (2021.emnlp-main)
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| Challenge: | Recent advances in transformer quantization have shown remarkable improvement in many Natural Language Processing tasks and beyond. |
| Approach: | They propose a novel quantization scheme for transformers that can be quantized to ultra-low bit-widths, leading to significant memory savings with a minimum accuracy loss. |
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Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer (2023.findings-emnlp)
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| Challenge: | Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. |
| Approach: | They propose a transformer variant with mixed attention spans that leverages the attention mechanism to capture long- and short-range dependencies in the sequence. |
| Outcome: | The proposed model can achieve competitive performance to models with full attention while reducing computational cost (75%) |
AdapterDrop: On the Efficiency of Adapters in Transformers (2021.emnlp-main)
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Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, Iryna Gurevych
| Challenge: | Recent approaches to transformer models are expensive to fine-tune, slow for inference, and have large storage requirements. |
| Approach: | They propose a method to remove adapters from transformer layers during training and inference . they show that AdapterDrop can dynamically reduce computational overhead . |
| Outcome: | The proposed approach reduces computational overhead while maintaining performance over multiple tasks with minimal loss of performance. |