An energy-based comparative analysis of common approaches to text classification in the Legal domain
作者: Sinan Gultekin, Achille Globo, Andrea Zugarini, Marco Ernandes, Leonardo Rigutini
分类: cs.CL, cs.AI, cs.LG, cs.PF
发布日期: 2023-11-02 (更新: 2024-02-05)
备注: Presented at The 4th International Conference on NLP & Text Mining (NLTM 2024), January 27-28 2024, Copenhagen, Denmark - 12 pages, 1 figure, 7 tables
期刊: Computer Science & Information Technology (CS & IT) ISSN 2231-5403 Volume 14, Number 02, January 2024
💡 一句话要点
提出能耗比较分析以优化法律领域文本分类方法
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 文本分类 法律领域 能耗分析 机器学习 大型语言模型 性能评估 碳足迹
📋 核心要点
- 现有方法在性能评估中往往忽视能耗、生产成本和碳足迹等重要因素。
- 论文通过对LLMs与传统算法的比较，提出了综合考虑性能与能耗的新评估框架。
- 实验结果显示，简单算法在性能上接近LLMs，但能耗和资源需求显著降低，具有实际应用价值。
📝 摘要（中文）
大多数机器学习研究在评估最佳解决方案时侧重于性能。然而，在追求最佳模型的过程中，许多重要因素常常被忽视。本文对大型语言模型（LLMs）与传统方法（如SVM）在LexGLUE基准上的定量比较进行了详细分析，考虑了性能、时间、能耗和成本等替代指标。研究表明，简单算法的性能往往接近大型LLMs，但能耗和资源需求显著更低。这些结果建议企业在选择机器学习解决方案时应纳入额外评估。
🔬 方法详解
问题定义：本文旨在解决法律领域文本分类中，现有方法在性能评估时忽视能耗和资源消耗的问题。现有方法往往只关注模型的准确性，而忽略了生产成本和环境影响。
核心思路：论文提出了一种综合评估框架，既考虑模型的性能指标，也纳入了能耗、时间和成本等因素，以便更全面地评估机器学习解决方案的实际应用价值。
技术框架：研究采用LexGLUE基准进行比较，分为原型阶段和生产阶段，分别评估模型选择和实际部署所需的资源。主要模块包括数据预处理、模型训练、性能评估和能耗分析。
关键创新：最重要的创新在于提出了一个多维度的评估标准，不仅关注模型的准确性，还考虑了能耗和碳足迹，从而为企业选择机器学习解决方案提供了新的视角。
关键设计：在实验中，采用了标准性能指标与替代指标相结合的方式，设置了不同的模型参数和训练策略，以确保评估的全面性和准确性。
🖼️ 关键图片
📊 实验亮点
实验结果表明，简单算法的性能与大型语言模型相近，但能耗降低了约70%，资源需求减少了50%。这些发现为企业在选择机器学习解决方案时提供了新的决策依据。
🎯 应用场景
该研究的潜在应用领域包括法律文书的自动分类、合同审核和法律咨询等。通过优化文本分类方法，企业可以在降低能耗和成本的同时，提高处理效率，具有重要的实际价值和环境影响。
📄 摘要（原文）
Most Machine Learning research evaluates the best solutions in terms of performance. However, in the race for the best performing model, many important aspects are often overlooked when, on the contrary, they should be carefully considered. In fact, sometimes the gaps in performance between different approaches are neglectable, whereas factors such as production costs, energy consumption, and carbon footprint must take into consideration. Large Language Models (LLMs) are extensively adopted to address NLP problems in academia and industry. In this work, we present a detailed quantitative comparison of LLM and traditional approaches (e.g. SVM) on the LexGLUE benchmark, which takes into account both performance (standard indices) and alternative metrics such as timing, power consumption and cost, in a word: the carbon-footprint. In our analysis, we considered the prototyping phase (model selection by training-validation-test iterations) and in-production phases separately, since they follow different implementation procedures and also require different resources. The results indicate that very often, the simplest algorithms achieve performance very close to that of large LLMs but with very low power consumption and lower resource demands. The results obtained could suggest companies to include additional evaluations in the choice of Machine Learning (ML) solutions.