| Challenge: | Understanding Transformer-based models has attracted significant attention . a zero-pass approach is feasible for some parameters, and for two-layer attention networks . |
| Approach: | They propose a theoretical framework where parameters of a trained Transformer are interpreted by projecting them into the embedding space. |
| Outcome: | The proposed framework shows that pre-trained and fine-tuned models can be interpreted in embedding space. |
Similar Papers
How to Dissect a Muppet: The Structure of Transformer Embedding Spaces (2022.tacl-1)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method outperforms recurrent architectures on a wide variety of tasks. |
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | a series of experiments show that fine-tuning only the cross-attention parameters is nearly as effective as fine-timing all parameters. |
| Approach: | They conduct experiments to fine-tune a translation model on data where either the source or target language has changed. |
| Outcome: | The proposed model can be trained to several new languages with reduced parameter storage overhead. |
The Hidden Space of Transformer Language Adapters (2024.acl-long)
Copied to clipboard
| Challenge: | Adapters are small modules trained on top of a frozen language model to adapt predictions to new target languages. |
| Approach: | They propose to train transformer language adapters on top of a frozen model to adapt predictions to new target languages. |
| Outcome: | The transformer language adapters are trained on top of a frozen model to adapt predictions to new target languages. |
Transformer-specific Interpretability (2024.eacl-tutorials)
Copied to clipboard
| Challenge: | Transformers are dominant play-ers in various scientific fields, but their inner workings remain opaque. |
| Approach: | This tutorial presents a trending approach to interpreting Transformers . it uses specific features of the Transformer architecture to quantify context- mixing interactions . |
| Outcome: | This tutorial aims to show how a new trending approach can be applied to Transformer-based models. |
Understanding and Overcoming the Challenges of Efficient Transformer Quantization (2021.emnlp-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed methods achieve state-of-the-art results on the GLUE benchmark using BERT, while preserving memory and accuracy. |
Exploring Quantization for Efficient Pre-Training of Transformer Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Quantization has proven to be effective after pre-training and during fine-tuning, but its effects on pre-trainer performance have remained unexplored. |
| Approach: | They propose a linear quantization strategy to be applied during the pre-training of Transformers to improve model efficiency and stability. |
| Outcome: | The proposed method improves model efficiency, stability, and performance while maintaining language modeling ability. |
Rethinking the Value of Transformer Components (2020.coling-main)
Copied to clipboard
| Challenge: | Empirical results show that certain components are more important than others . we propose a new training strategy that can improve Transformer models by distinguishing unimportant components . |
| Approach: | They propose a training strategy that distinguishes the unimportant components in training . they compare the impact of individual component (sub-layer) on model performance . |
| Outcome: | The proposed training strategy can improve translation performance by distinguishing unimportant components in training. |
What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on pre-trained Transformers has focused on learning the meaning of positions . Embedding the position information in the self-attention mechanism is also an indispensable factor in NLP . |
| Approach: | They propose to use feature-level analysis to examine pre-trained Transformers' position embeddings . they also use empirical experiments to determine the appropriate positional encoding function . |
| Outcome: | The results of the empirical study can guide future work to choose the appropriate positional encoding function for specific tasks. |
Too Much in Common: Shifting of Embeddings in Transformer Language Models and its Implications (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing studies have shown that word embeddings do not occupy a narrow cone, but rather drift in common directions. |
| Approach: | They show that anisotropy can be restored using a simple transformation of word embeddings. |
| Outcome: | The proposed model can restore anisotropy using a simple transformation. |
HashFormers: Towards Vocabulary-independent Pre-trained Transformers (2022.emnlp-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed models are more memory efficient than existing models while achieving comparable performance on multiple text classification tasks. |