Attention Condensation via Sparsity Induced Regularized Training

• URL: http://arxiv.org/abs/2503.01564v2
• Date: Wed, 12 Mar 2025 18:12:59 GMT
• Title: Attention Condensation via Sparsity Induced Regularized Training
• Authors: Eli Sason, Darya Frolova, Boris Nazarov, Felix Goldberd,
• Abstract summary: Self-attention dominates the transformer's inference time as the context window expands.<n>We extend a theoretical framework of attention sparsity in Large Language Models.<n>A customized loss function is designed to enforce the sparsity by restricting the number of top elements in the attention matrix.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As the context window expands, self-attention increasingly dominates the transformer's inference time. Therefore, accelerating attention computation while minimizing performance degradation is essential for the efficient deployment of Large Language Models (LLMs). In this study we extend a theoretical framework of attention sparsity in LLMs. A customized loss function is designed to enforce the sparsity by restricting the number of top elements in the attention matrix. We perform an initial set of evaluations with GPT-2 to show the effectiveness of our sparsification approach. The attention matrices of the models trained with the proposed loss are both sparse and effective in capturing relevant input dependencies. We now continue working to demonstrate the value of our approach on larger models and different architectures.

Related papers

• R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference [77.47238561728459]
  R-Sparse is a training-free activation sparsity approach capable of achieving high sparsity levels in advanced LLMs. Experiments on Llama-2/3 and Mistral models across ten diverse tasks demonstrate that R-Sparse achieves comparable performance at 50% model-level sparsity.
  arXiv  Detail & Related papers  (2025-04-28T03:30:32Z)
• Efficient Diffusion as Low Light Enhancer [63.789138528062225]
  Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images. textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
  arXiv  Detail & Related papers  (2024-10-16T08:07:18Z)
• EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models [29.57891007810509]
  Large Language Models (LLMs) have demonstrated outstanding performance across a variety of natural language processing tasks. We introduce EchoAtt, a novel framework aimed at optimizing transformer-based models by analyzing and leveraging the similarity of attention patterns across layers. Our best results with TinyLLaMA-1.1B demonstrate that EchoAtt improves inference speed by 15%, training speed by 25%, and reduces the number of parameters by approximately 4%, all while improving zero-shot performance.
  arXiv  Detail & Related papers  (2024-09-22T21:08:37Z)
• ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect [2.3961612657966946]
  Outlier detection (OD) is the task of identifying unusual observations (or outliers) from a given or upcoming data.<n>Inlier-memorization (IM) effect suggests that generative models memorize inliers before outliers in early learning stages.<n>We propose a theoretically principled method to address UOD tasks by maximally utilizing the IM effect.
  arXiv  Detail & Related papers  (2024-08-19T08:40:53Z)
• Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization [77.62516752323207]
  We introduce an orthogonal fine-tuning method for efficiently fine-tuning pretrained weights and enabling enhanced robustness and generalization. A self-regularization strategy is further exploited to maintain the stability in terms of zero-shot generalization of VLMs, dubbed OrthSR. For the first time, we revisit the CLIP and CoOp with our method to effectively improve the model on few-shot image classficiation scenario.
  arXiv  Detail & Related papers  (2024-07-11T10:35:53Z)
• Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization [0.6445087473595953]
  Large language models (LLMs) demonstrate outstanding performance in various tasks in machine learning. deploying LLM inference poses challenges due to the high compute and memory requirements. We present Tender, an algorithm-hardware co-design solution that enables efficient deployment of LLM inference at low precision.
  arXiv  Detail & Related papers  (2024-06-16T09:51:55Z)
• How Sparse Attention Approximates Exact Attention? Your Attention is Naturally $n^C$-Sparse [9.552839922307587]
  Sparse Attention is a technique that approximates standard attention computation with sub-quadratic complexity.<n> Variations of this technique, such as pruning KV cache, sparsity-based fast attention, and Sparse Transformer, have been extensively utilized for efficient Large Language Models (LLMs) deployment.
  arXiv  Detail & Related papers  (2024-04-03T12:37:34Z)
• Extending Token Computation for LLM Reasoning [5.801044612920816]
  Large Language Models (LLMs) are pivotal in advancing natural language processing. LLMs often struggle with complex reasoning tasks due to inefficient attention distributions. We introduce a novel method for extending computed tokens in the Chain-of-Thought process, utilizing attention mechanism optimization.
  arXiv  Detail & Related papers  (2024-03-22T03:23:58Z)
• ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization [52.5587113539404]
  We introduce a causality-aware entropy term that effectively identifies and prioritizes actions with high potential impacts for efficient exploration. Our proposed algorithm, ACE: Off-policy Actor-critic with Causality-aware Entropy regularization, demonstrates a substantial performance advantage across 29 diverse continuous control tasks.
  arXiv  Detail & Related papers  (2024-02-22T13:22:06Z)
• Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use [74.72150542395487]
  An inherent waveform pattern in the attention allocation of large language models (LLMs) significantly affects their performance in tasks demanding a high degree of context awareness. To address this issue, we propose a novel inference method named Attention Buckets.
  arXiv  Detail & Related papers  (2023-12-07T17:24:51Z)
• Augmenting Unsupervised Reinforcement Learning with Self-Reference [63.68018737038331]
  Humans possess the ability to draw on past experiences explicitly when learning new tasks. We propose the Self-Reference (SR) approach, an add-on module explicitly designed to leverage historical information. Our approach achieves state-of-the-art results in terms of Interquartile Mean (IQM) performance and Optimality Gap reduction on the Unsupervised Reinforcement Learning Benchmark.
  arXiv  Detail & Related papers  (2023-11-16T09:07:34Z)
• Gradient constrained sharpness-aware prompt learning for vision-language models [99.74832984957025]
  This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM) By analyzing the loss landscapes of the state-of-the-art method and vanilla Sharpness-aware Minimization (SAM) based method, we conclude that the trade-off performance correlates to both loss value and loss sharpness. We propose a novel SAM-based method for prompt learning, denoted as Gradient Constrained Sharpness-aware Context Optimization (GCSCoOp)
  arXiv  Detail & Related papers  (2023-09-14T17:13:54Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.