Achieving Sparse Activation in Small Language Models

• URL: http://arxiv.org/abs/2406.06562v1
• Date: Mon, 3 Jun 2024 03:21:49 GMT
• Title: Achieving Sparse Activation in Small Language Models
• Authors: Jifeng Song, Kai Huang, Xiangyu Yin, Boyuan Yang, Wei Gao,
• Abstract summary: Sparse activation is a useful technique to reduce the computing cost of Large Language Models (LLMs) without retraining or adaptation efforts. In this paper, we aim to achieve sparse activation in Small Language Models (SLMs) We first show that the existing sparse activation schemes in LLMs that build on neurons' output magnitudes cannot be applied to SLMs, and activating neurons based on their attribution scores is a better alternative.
• Score: 9.05326883263473
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sparse activation, which selectively activates only an input-dependent set of neurons in inference, is a useful technique to reduce the computing cost of Large Language Models (LLMs) without retraining or adaptation efforts. However, whether it can be applied to the recently emerging Small Language Models (SLMs) remains questionable, because SLMs are generally less over-parameterized than LLMs. In this paper, we aim to achieve sparse activation in SLMs. We first show that the existing sparse activation schemes in LLMs that build on neurons' output magnitudes cannot be applied to SLMs, and activating neurons based on their attribution scores is a better alternative. Further, we demonstrated and quantified the large errors of existing attribution metrics when being used for sparse activation, due to the interdependency among attribution scores of neurons across different layers. Based on these observations, we proposed a new attribution metric that can provably correct such errors and achieve precise sparse activation. Experiments over multiple popular SLMs and datasets show that our approach can achieve 80% sparsification ratio with <5% model accuracy loss, comparable to the sparse activation achieved in LLMs. The source code is available at: https://github.com/pittisl/Sparse-Activation.

Related papers

• Can a Large Language Model Learn Matrix Functions In Context? [3.7478782183628634]
  Large Language Models (LLMs) have demonstrated the ability to solve complex tasks through In-Context Learning (ICL) This paper explores the capacity of LLMs to solve non-linear numerical computations, with specific emphasis on functions of the Singular Value Decomposition.
  arXiv  Detail & Related papers  (2024-11-24T00:33:43Z)
• Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output [49.893971654861424]
  We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG) We compute a factuality score that can be thresholded to yield a binary decision. Our experiments show high area under the ROC curve (AUC) across a wide range of relevant open source datasets.
  arXiv  Detail & Related papers  (2024-11-01T20:44:59Z)
• Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification? [2.1861408994125253]
  Large Language Models (LLM) have recently shown promising performance in temporal reasoning tasks. Recent studies have tested the LLMs' performance in detecting temporal relations of closed-source models only.
  arXiv  Detail & Related papers  (2024-10-14T13:10:45Z)
• Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing [63.20133320524577]
  Large Language Models (LLMs) have demonstrated great potential as generalist assistants. It is crucial that these models exhibit desirable behavioral traits, such as non-toxicity and resilience against jailbreak attempts. In this paper, we observe that directly editing a small subset of parameters can effectively modulate specific behaviors of LLMs.
  arXiv  Detail & Related papers  (2024-07-11T17:52:03Z)
• DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph [70.79413606968814]
  We introduce Dynamic Evaluation of LLMs via Adaptive Reasoning Graph Evolvement (DARG) to dynamically extend current benchmarks with controlled complexity and diversity. Specifically, we first extract the reasoning graphs of data points in current benchmarks and then perturb the reasoning graphs to generate novel testing data. Such newly generated test samples can have different levels of complexity while maintaining linguistic diversity similar to the original benchmarks.
  arXiv  Detail & Related papers  (2024-06-25T04:27:53Z)
• Simple and Scalable Strategies to Continually Pre-train Large Language Models [20.643648785602462]
  Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes available. We show that a simple and scalable combination of learning rate re-warming, LR re-decaying, and replay of previous data is sufficient to match the performance of fully re-training from scratch.
  arXiv  Detail & Related papers  (2024-03-13T17:58:57Z)
• ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs [91.31204876440765]
  We introduce a general method that defines neuron activation through neuron output magnitudes and a tailored magnitude threshold. To find the most efficient activation function for sparse computation, we propose a systematic framework. We conduct thorough experiments on LLMs utilizing different activation functions, including ReLU, SwiGLU, ReGLU, and ReLU$2$.
  arXiv  Detail & Related papers  (2024-02-06T08:45:51Z)
• Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs [67.38165028487242]
  We introduce Dynamic Sparse No Training (DSnoT), a training-free fine-tuning approach to fine-tune large language models (LLMs) Inspired by the Dynamic Sparse Training, DSnoT minimizes the reconstruction error between the dense and sparse LLMs. Our paper offers fresh insights into how to fine-tune sparse LLMs in an efficient training-free manner and open new venues to scale the great potential of sparsity to LLMs.
  arXiv  Detail & Related papers  (2023-10-13T07:38:52Z)
• Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling [95.9542389945259]
  Sentence scoring aims at measuring the likelihood of a sentence and is widely used in many natural language processing scenarios. We propose textitTranscormer -- a Transformer model with a novel textitsliding language modeling (SLM) for sentence scoring.
  arXiv  Detail & Related papers  (2022-05-25T18:00:09Z)

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.