Characterizing stable regions in the residual stream of LLMs

• URL: http://arxiv.org/abs/2409.17113v4
• Date: Mon, 18 Nov 2024 10:32:32 GMT
• Title: Characterizing stable regions in the residual stream of LLMs
• Authors: Jett Janiak, Jacek Karwowski, Chatrik Singh Mangat, Giorgi Giglemiani, Nora Petrova, Stefan Heimersheim,
• Abstract summary: We identify stable regions in the residual stream of Transformers, where the model's output remains insensitive to small activation changes. These regions emerge during training and become more defined as training progresses or model size increases.
• Score: 0.0
• License:
• Abstract: We identify stable regions in the residual stream of Transformers, where the model's output remains insensitive to small activation changes, but exhibits high sensitivity at region boundaries. These regions emerge during training and become more defined as training progresses or model size increases. The regions appear to be much larger than previously studied polytopes. Our analysis suggests that these stable regions align with semantic distinctions, where similar prompts cluster within regions, and activations from the same region lead to similar next token predictions. This work provides a promising research direction for understanding the complexity of neural networks, shedding light on training dynamics, and advancing interpretability.

Related papers

• Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning [50.88504784466931]
  Multi-task dense prediction involves semantic segmentation, depth estimation, and surface normal estimation. Existing solutions typically rely on learning global image representations for global cross-task image matching. Our proposal involves modeling region-wise representations using Gaussian Distributions.
  arXiv  Detail & Related papers  (2024-03-15T12:41:30Z)
• Structure of activity in multiregion recurrent neural networks [2.1756081703276]
  We study the dynamics of neural networks with multiple interconnected regions. Within each region, neurons have a combination of random and structured recurrent connections. We show that taming the complexity of activity within a region is necessary for it to route signals to and from other regions.
  arXiv  Detail & Related papers  (2024-02-19T14:51:55Z)
• Hard Region Aware Network for Remote Sensing Change Detection [44.269913858088614]
  Change detection (CD) is essential for various real-world applications, such as urban management and disaster assessment. This paper proposes a novel change detection network, termed as HRANet, which provides accurate change maps via hard region mining.
  arXiv  Detail & Related papers  (2023-05-31T02:52:38Z)
• Analyzing the Domain Shift Immunity of Deep Homography Estimation [1.4607247979144045]
  CNN-driven homography estimation models show a distinctive immunity to domain shifts. This study explores the resilience of a variety of deep homography estimation models to domain shifts.
  arXiv  Detail & Related papers  (2023-04-19T21:28:31Z)
• Understanding the Evolution of Linear Regions in Deep Reinforcement Learning [21.53394095184201]
  We study how observed region counts and their densities evolve during deep reinforcement learning. We find that the region density increases only moderately throughout training, as measured along fixed trajectories coming from the final policy. Our findings suggest that the complexity of deep reinforcement learning policies does not principally emerge from a significant growth in the complexity of functions observed on-and-around trajectories of the policy.
  arXiv  Detail & Related papers  (2022-10-24T21:22:12Z)
• Region Rebalance for Long-Tailed Semantic Segmentation [89.84860341946283]
  We first investigate and identify the main challenges of addressing this issue through pixel rebalance. Then a simple and yet effective region rebalance scheme is derived based on our analysis. With the proposed region rebalance scheme, state-of-the-art BEiT receives +0.7% gain in terms of mIoU on the ADE20K val set.
  arXiv  Detail & Related papers  (2022-04-05T03:47:47Z)
• Region-Based Semantic Factorization in GANs [67.90498535507106]
  We present a highly efficient algorithm to factorize the latent semantics learned by Generative Adversarial Networks (GANs) concerning an arbitrary image region. Through an appropriately defined generalized Rayleigh quotient, we solve such a problem without any annotations or training. Experimental results on various state-of-the-art GAN models demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-02-19T17:46:02Z)
• Point-Level Region Contrast for Object Detection Pre-Training [147.47349344401806]
  We present point-level region contrast, a self-supervised pre-training approach for the task of object detection. Our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality.
  arXiv  Detail & Related papers  (2022-02-09T18:56:41Z)
• What training reveals about neural network complexity [80.87515604428346]
  This work explores the hypothesis that the complexity of the function a deep neural network (NN) is learning can be deduced by how fast its weights change during training. Our results support the hypothesis that good training behavior can be a useful bias towards good generalization.
  arXiv  Detail & Related papers  (2021-06-08T08:58:00Z)
• Adaptive Region-Based Active Learning [57.78835999208091]
  We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions. We prove theoretical guarantees for both the generalization error and the label complexity of our algorithm. We report the results of an extensive suite of experiments on several real-world datasets.
  arXiv  Detail & Related papers  (2020-02-18T03:16:36Z)
• Empirical Studies on the Properties of Linear Regions in Deep Neural Networks [34.08593191989188]
  A deep neural network (DNN) with piecewise linear activations can partition the input space into numerous small linear regions. It is believed that the number of these regions represents the expressivity of the DNN. We study their local properties, such as the inspheres, the directions of the corresponding hyperplanes, the decision boundaries, and the relevance of the surrounding regions.
  arXiv  Detail & Related papers  (2020-01-04T12:47:58Z)

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.