Citation: A Key to Building Responsible and Accountable Large Language Models¶
Jie Huang, Kevin Chen-Chuan Chang
引文：构建负责任和负责任的大型语言模型的关键
Large Language Models (LLMs) bring transformative benefits alongside unique challenges, including intellectual property (IP) and ethical concerns. This position paper explores a novel angle to mitigate these risks, drawing parallels between LLMs and established web systems. We identify “citation” - the acknowledgement or reference to a source or evidence - as a crucial yet missing component in LLMs. Incorporating citation could enhance content transparency and verifiability, thereby confronting the IP and ethical issues in the deployment of LLMs. We further propose that a comprehensive citation mechanism for LLMs should account for both non-parametric and parametric content. Despite the complexity of implementing such a citation mechanism, along with the potential pitfalls, we advocate for its development. Building on this foundation, we outline several research problems in this area, aiming to guide future explorations towards building more responsible and accountable LLMs.
大型语言模型 （LLMs） 带来了变革性的好处，同时也带来了独特的挑战，包括知识产权 （IP） 和道德问题。本立场文件探讨了减轻这些风险的新角度，在已建立的 Web 系统之间LLMs进行了类比。我们将“引用”（对来源或证据的确认或引用）确定为中LLMs一个关键但缺失的组成部分。纳入引文可以提高内容的透明度和可验证性，从而在部署引文时面临知识产权和伦理问题LLMs。我们进一步提出，应为非参数和参数内容提供全面的引用机制LLMs。尽管实施这种引用机制很复杂，也存在潜在的陷阱，但我们还是主张发展这种机制。在此基础上，我们概述了该领域的几个研究问题，旨在指导未来探索，建设更加负责任和负责任的人LLMs。