On Elastic Language Models

📄 arXiv: 2311.07204v1 📥 PDF

作者: Chen Zhang, Benyou Wang, Dawei Song

分类: cs.IR, cs.LG

发布日期: 2023-11-13

备注: 27 pages, 11 figures, 9 tables

💡 一句话要点

提出弹性语言模型以解决请求流变化问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 弹性语言模型 知识蒸馏 信息检索 动态请求流 计算弹性 自然语言处理 性能优化

📋 核心要点

  1. 现有的压缩语言模型在请求数量变化时，静态的延迟-性能权衡可能导致性能不足或延迟过高。
  2. 本文提出的弹性语言模型（ElasticLM）通过引入计算弹性，能够根据请求流动态调整性能与延迟的权衡。
  3. 实验结果显示，ElasticLM在GLUE基准和多个信息检索任务中表现优异，能够与静态基线模型竞争。

📝 摘要（中文）

大规模预训练语言模型在语言理解和信息检索任务中表现出色。知识蒸馏为压缩大型语言模型提供了机会，以实现合理的延迟-性能权衡。然而，在请求数量高度变化的场景中，静态的权衡可能不适用。为此，本文提出了一种弹性语言模型（ElasticLM），能够根据请求流动态调整权衡。通过引入计算弹性，ElasticLM能够在可扩展和可控的计算资源下实时调整性能。实验结果表明，ElasticLM在多个基准任务中表现出色，能够有效应对请求流的变化。

🔬 方法详解

问题定义：现有的压缩语言模型在请求流变化时，无法灵活调整延迟与性能的权衡，导致在请求量大时延迟过高，或在请求量小时性能不足。

核心思路：本文提出的弹性语言模型（ElasticLM）通过引入计算弹性，允许模型在运行时根据请求流动态调整性能与延迟的权衡，从而提高模型的适应性和效率。

技术框架：ElasticLM的整体架构包括弹性结构设计和弹性优化算法。弹性结构使得模型能够在不同计算资源下灵活调整，而弹性优化则用于在计算弹性下训练模型。

关键创新：ElasticLM的最大创新在于其计算弹性设计，使得模型能够实时适应请求流的变化，这与传统静态模型形成鲜明对比。

关键设计：在模型设计中，采用了特定的损失函数和网络结构，以支持弹性计算。同时，设计了弹性调度策略，以优化模型在不同请求量下的表现。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，ElasticLM在GLUE基准测试中表现优异，并在Natural Question、Trivia QA和MS MARCO等信息检索任务中与多种静态基线模型竞争。在线模拟实验显示，ElasticLM能够根据请求流变化提供灵活的延迟-性能权衡，显著提升了系统的响应能力。

🎯 应用场景

该研究的潜在应用领域包括搜索引擎、智能问答系统和其他需要处理动态请求流的自然语言处理任务。通过提高模型的适应性，ElasticLM能够在实际应用中显著提升用户体验和系统效率。未来，该技术可能推动更多智能系统的实时响应能力。

📄 摘要（原文）

Large-scale pretrained language models have achieved compelling performance in a wide range of language understanding and information retrieval tasks. Knowledge distillation offers an opportunity to compress a large language model to a small one, in order to reach a reasonable latency-performance tradeoff. However, for scenarios where the number of requests (e.g., queries submitted to a search engine) is highly variant, the static tradeoff attained by the compressed language model might not always fit. Once a model is assigned with a static tradeoff, it could be inadequate in that the latency is too high when the number of requests is large or the performance is too low when the number of requests is small. To this end, we propose an elastic language model (ElasticLM) that elastically adjusts the tradeoff according to the request stream. The basic idea is to introduce a compute elasticity to the compressed language model, so that the tradeoff could vary on-the-fly along scalable and controllable compute. Specifically, we impose an elastic structure to enable ElasticLM with compute elasticity and design an elastic optimization to learn ElasticLM under compute elasticity. To serve ElasticLM, we apply an elastic schedule. Considering the specificity of information retrieval, we adapt ElasticLM to dense retrieval and reranking and present ElasticDenser and ElasticRanker respectively. Offline evaluation is conducted on a language understanding benchmark GLUE; and several information retrieval tasks including Natural Question, Trivia QA, and MS MARCO. The results show that ElasticLM along with ElasticDenser and ElasticRanker can perform correctly and competitively compared with an array of static baselines. Furthermore, online simulation with concurrency is also carried out. The results demonstrate that ElasticLM can provide elastic tradeoffs with respect to varying request stream.