Generalized Attention Flow: Feature Attribution for Transformer Models via Maximum Flow (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to feature attributions rely on attention weights and attention weightings. |
| Approach: | They propose a feature attribution method that replaces attention weights with the generalized Information Tensor to enhance the performance of Transformer-based models. |
| Outcome: | The proposed method outperforms state-of-the-art feature attribution methods on sequence classification tasks and provides a more reliable interpretation of Transformer model outputs. |
Similar Papers
TensorLens: End-to-End Transformer Analysis via High-Order Attention Tensors (2026.acl-long)
Copied to clipboard
| Challenge: | Existing attention-aggregation methods focus on individual attention heads or layers, failing to account for the model’s global behavior. |
| Approach: | They propose a unified attention representation that captures the entire transformer as a single, input-dependent linear operator expressed through a high-order attention-interaction tensor. |
| Outcome: | The proposed model encapsulates the entire transformer as a single, input-dependent linear operator expressed through a high-order attention-interaction tensor. |
Quantifying Attention Flow in Transformers (2020.acl-main)
Copied to clipboard
| Challenge: | In the Transformer model, “self-attention” combines information from attended embeddings into the representation of the focal embeddable in the next layer. |
| Approach: | They propose two methods to quantify flow of information through self-attention using attention weights as relative relevance of input tokens. |
| Outcome: | The proposed methods give complementary views on the flow of information and yield higher correlations with importance scores of input tokens. |
PR-XAI: PageRank-Based Feature Attribution for Transformers (2026.acl-long)
Copied to clipboard
| Challenge: | Existing feature attribution methods for transformer models suffer from limitations that undermine their efficacy. |
| Approach: | They propose a feature attribution method for transformer models based on PageRank . they propose attribution methods that apply PageRank to attention-derived graphs . |
| Outcome: | The proposed method outperforms state-of-the-art methods in faithfulness and classification metrics with significant gains on long-form text. |
A Multiscale Visualization of Attention in the Transformer Model (P19-3)
Copied to clipboard
| Challenge: | Various tools have been developed to visualize attention in NLP models, ranging from attention-matrix heatmaps to bipartite graph representations. |
| Approach: | They propose an open-source tool that visualizes attention at multiple scales and provides a unique perspective on the attention mechanism. |
| Outcome: | The proposed model outperforms OpenAI GPT-2 and BERT on several language modeling benchmarks. |
Modeling Recurrence for Transformer (N19-1)
Copied to clipboard
| Challenge: | Existing studies show that the lack of recurrence modeling hinders the development of a translation model. |
| Approach: | They propose to model recurrence for Transformer with an additional recurrent encoder. |
| Outcome: | The proposed model outperforms the deep model on EnglishGerman and ChineseEnglish translation tasks. |
Sparsifying Transformer Models with Trainable Representation Pooling (2022.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to sparsify attention in the Transformer model are based on quadratic memory complexity and a lack of information for each word. |
| Approach: | They propose a method to sparsify attention in a Transformer model by learning to select the most-informative token representations during the training process. |
| Outcome: | The proposed model performs better than the current SOTA model while being 1.8 faster during training, 4.5 faster inference and 13 more efficient in the decoder. |
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel (D19-1)
Copied to clipboard
| Challenge: | Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. |
| Approach: | They propose a new formulation of attention via the lens of the kernel which allows us to understand individual components of Transformer's attention. |
| Outcome: | The proposed model outperforms existing models on language understanding and sequence prediction tasks and is more efficient than existing models. |
Attention Calibration for Transformer in Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Attention mechanisms have been ubiquitous in neural machine translation (NMT) however, many studies doubt whether highlyattended inputs have a large impact on the model outputs. |
| Approach: | They propose to introduce a mask perturbation model that automatically evaluates each input’s contribution to the model outputs. |
| Outcome: | The proposed model is more uniform at lower layers while more concentrated on the specific inputs at higher layers. |
Adaptive Attention Span in Transformers (P19-1)
Copied to clipboard
| Challenge: | We extend the maximum context size of a neural network called Transformer to 8k characters. |
| Approach: | They propose a self-attention mechanism that can learn its optimal attention span . this allows for models with longer context and the capability to catch longer dependencies. |
| Outcome: | The proposed model achieves state-of-the-art performance on text8 and enwiki8 using 8k characters with no loss of performance, and maintains control over memory footprint and computational time. |
VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work in interpretability suggests we can project weights and hidden states of transformer-based language models (LMs) to their vocabulary space, a transformation that makes them more human interpretable. |
| Approach: | They propose a tool to visualize a forward pass of Generative Pre-trained Transformers as an interactive flow graph with nodes representing neurons or hidden states and edges representing interactions between them. |
| Outcome: | The proposed visualization simplifies huge amounts of data into easy-to-read graphs that can reflect the models’ internal processing, uncovering the contribution of each component to the models' final prediction. |