Regulation and NLP (RegNLP): Taming Large Language Models

📄 arXiv: 2310.05553v1 📥 PDF

作者: Catalina Goanta, Nikolaos Aletras, Ilias Chalkidis, Sofia Ranchordas, Gerasimos Spanakis

分类: cs.CL

发布日期: 2023-10-09

备注: 9 pages, long paper at EMNLP 2023 proceedings

💡 一句话要点

提出RegNLP以解决大型语言模型的监管与风险问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 自然语言处理 人工智能 监管研究 风险评估 多学科研究 AI伦理 政策制定

📋 核心要点

  1. 现有的NLP研究在风险评估方面缺乏系统性方法，导致科学完整性受到威胁。
  2. 论文提出将NLP研究与监管研究相结合，利用监管研究的知识来系统性地处理风险与不确定性。
  3. 通过倡导RegNLP的建立，推动科学知识与监管过程的连接，提升NLP领域的研究质量。

📝 摘要（中文）

自然语言处理（NLP）和人工智能（AI）的科学创新正处于快速发展之中。大型语言模型（LLMs）的出现引发了关于其开发、部署和使用的利益与风险的重要讨论。目前，这些讨论往往被AI安全和伦理运动主导，导致了极化的叙述，影响了AI监管和治理的政治议程。本文主张NLP研究应更紧密地与监管研究相结合，借鉴其系统性方法论，以科学地评估和比较监管选项，从而推动一个新的多学科研究领域——RegNLP的发展。

🔬 方法详解

问题定义：本文旨在解决NLP领域在风险评估和监管方面的不足，现有方法往往缺乏系统性和科学依据，导致监管措施的有效性受到质疑。

核心思路：论文提出通过将NLP研究与监管研究相结合，利用后者的系统性方法论来评估和比较不同的监管选项，从而提升NLP研究的科学性和实用性。

技术框架：整体架构包括对监管基本原则的讨论、风险与不确定性的分析，以及对现有NLP风险评估讨论的不足之处的反思，最终形成一个新的多学科研究空间。

关键创新：最重要的创新点在于提出了RegNLP这一概念，强调NLP研究应借鉴监管研究的系统性方法，从而更有效地应对AI技术带来的风险与挑战。

关键设计：在设计上，论文强调了对监管研究的深入理解，提出了将科学证据与监管过程相结合的必要性，具体参数和方法尚未详细列出，待后续研究进一步探讨。

🖼️ 关键图片

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📊 实验亮点

论文强调了RegNLP的建立将为NLP研究提供新的视角，促进科学与监管的结合，尽管具体实验结果尚未提供，但其理论框架为未来的实证研究奠定了基础。

🎯 应用场景

该研究的潜在应用领域包括AI技术的监管政策制定、NLP系统的风险管理以及相关法律法规的完善。通过建立RegNLP，研究者可以更有效地将科学发现转化为实际的监管措施，促进AI技术的安全发展。

📄 摘要（原文）

The scientific innovation in Natural Language Processing (NLP) and more broadly in artificial intelligence (AI) is at its fastest pace to date. As large language models (LLMs) unleash a new era of automation, important debates emerge regarding the benefits and risks of their development, deployment and use. Currently, these debates have been dominated by often polarized narratives mainly led by the AI Safety and AI Ethics movements. This polarization, often amplified by social media, is swaying political agendas on AI regulation and governance and posing issues of regulatory capture. Capture occurs when the regulator advances the interests of the industry it is supposed to regulate, or of special interest groups rather than pursuing the general public interest. Meanwhile in NLP research, attention has been increasingly paid to the discussion of regulating risks and harms. This often happens without systematic methodologies or sufficient rooting in the disciplines that inspire an extended scope of NLP research, jeopardizing the scientific integrity of these endeavors. Regulation studies are a rich source of knowledge on how to systematically deal with risk and uncertainty, as well as with scientific evidence, to evaluate and compare regulatory options. This resource has largely remained untapped so far. In this paper, we argue how NLP research on these topics can benefit from proximity to regulatory studies and adjacent fields. We do so by discussing basic tenets of regulation, and risk and uncertainty, and by highlighting the shortcomings of current NLP discussions dealing with risk assessment. Finally, we advocate for the development of a new multidisciplinary research space on regulation and NLP (RegNLP), focused on connecting scientific knowledge to regulatory processes based on systematic methodologies.