RAEE: A Training-Free Retrieval-Augmented Early Exiting Framework for Efficient Inference

• URL: http://arxiv.org/abs/2405.15198v1
• Date: Fri, 24 May 2024 04:01:24 GMT
• Title: RAEE: A Training-Free Retrieval-Augmented Early Exiting Framework for Efficient Inference
• Authors: Lianming Huang, Shangyu Wu, Yufei Cui, Ying Xiong, Xue Liu, Tei-Wei Kuo, Nan Guan, Chun Jason Xue,
• Abstract summary: This paper proposes RAEE, a training-free Retrieval-Augmented Early Exiting framework for efficient inference. Experimental results demonstrate that the proposed RAEE can significantly accelerate inference. RAEE also achieves state-of-the-art zero-shot performance on 8 classification tasks.
• Score: 20.250550771195726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying large language model inference remains challenging due to their high computational overhead. Early exiting accelerates model inference by adaptively reducing the number of inference layers. Existing methods require training internal classifiers to determine whether to exit at each intermediate layer. However, such classifier-based early exiting frameworks require significant effort to design and train the classifiers. To address these limitations, this paper proposes RAEE, a training-free Retrieval-Augmented Early Exiting framework for efficient inference. First, this paper demonstrates that the early exiting problem can be modeled as a distribution prediction problem, where the distribution is approximated using similar data's existing information. Next, the paper details the process of collecting existing information to build the retrieval database. Finally, based on the pre-built retrieval database, RAEE leverages the retrieved similar data's exiting information to guide the backbone model to exit at the layer, which is predicted by the approximated distribution. Experimental results demonstrate that the proposed RAEE can significantly accelerate inference. RAEE also achieves state-of-the-art zero-shot performance on 8 classification tasks.

Related papers

• Data Shapley in One Training Run [88.59484417202454]
  Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. Existing approaches require re-training models on different data subsets, which is computationally intensive. This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest.
  arXiv  Detail & Related papers  (2024-06-16T17:09:24Z)
• DAISY: Data Adaptive Self-Supervised Early Exit for Speech Representation Models [55.608981341747246]
  We introduce Data Adaptive Self-Supervised Early Exit (DAISY), an approach that decides when to exit based on the self-supervised loss. Our analysis on the adaptivity of DAISY shows that the model exits early (using fewer layers) on clean data while exits late (using more layers) on noisy data.
  arXiv  Detail & Related papers  (2024-06-08T12:58:13Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• DRoP: Distributionally Robust Pruning [11.930434318557156]
  We conduct the first systematic study of the impact of data pruning on classification bias of trained models. We propose DRoP, a distributionally robust approach to pruning and empirically demonstrate its performance on standard computer vision benchmarks.
  arXiv  Detail & Related papers  (2024-04-08T14:55:35Z)
• DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks [43.967626080432275]
  We propose a novel Distance-Enhanced Early Exiting framework for BERT (DE$3$-BERT) We implement a hybrid exiting strategy that supplements classic entropy-based local information with distance-based global information. Experiments on the GLUE benchmark demonstrate that DE$3$-BERT consistently outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2024-02-03T15:51:17Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Training dynamic models using early exits for automatic speech recognition on resource-constrained devices [15.879328412777008]
  Early-exit architectures enable the development of dynamic models capable of adapting their size and architecture to varying levels of computational resources and ASR performance demands. We show that early-exit models trained from scratch not only preserve performance when using fewer encoder layers but also exhibit enhanced task accuracy compared to single-exit or pre-trained models. Results provide insights into the training dynamics of early-exit architectures for ASR models.
  arXiv  Detail & Related papers  (2023-09-18T07:45:16Z)
• Sequential Learning Of Neural Networks for Prequential MDL [18.475866691786695]
  We evaluate approaches for computing prequential description lengths for image classification datasets with neural networks. Considering the computational cost, we find that online-learning with rehearsal has favorable performance. We present description lengths for a suite of image classification datasets that improve upon previously reported results by large margins.
  arXiv  Detail & Related papers  (2022-10-14T16:30:23Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)

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.