研究大型语言模型生成文本的理论与方法论框架
Theoretical and Methodological Framework for Studying Texts Produced by Large Language Models
AI总结：
本文提出从计量语言学视角研究LLMs及其生成文本的理论与方法论框架，区分模型基底与模拟实体，主张非拟人化方法，并呼吁构建关于LLM文本特征的理论及探索其作为人类文化研究工具的潜力。
摘要：
本文探讨了从计量语言学视角研究大型语言模型（LLMs）及其生成文本时在概念、方法论和技术上面临的挑战。它建立在区分作为基底的LLM与模型所模拟实体的理论框架之上。本文提倡对模型采取严格的非拟人化方法，同时谨慎地将研究人类语言行为的方法论应用于被模拟实体。自然语言处理研究者关注模型本身、其架构、评估及提升性能的方法，而作为计量语言学家，我们应致力于构建关于LLMs生成文本特征、其与人类生成文本的差异以及被模拟实体属性的稳健理论。此外，我们应探索LLMs作为研究人类文化（语言是其不可或缺的一部分）工具的潜力。
英文摘要：
This paper addresses the conceptual, methodological and technical challenges in studying large language models (LLMs) and the texts they produce from a quantitative linguistics perspective. It builds on a theoretical framework that distinguishes between the LLM as a substrate and the entities the model simulates. The paper advocates for a strictly non-anthropomorphic approach to models while cautiously applying methodologies used in studying human linguistic behavior to the simulated entities. While natural language processing researchers focus on the models themselves, their architecture, evaluation, and methods for improving performance, we as quantitative linguists should strive to build a robust theory concerning the characteristics of texts produced by LLMs, how they differ from human-produced texts, and the properties of simulated entities. Additionally, we should explore the potential of LLMs as an instrument for studying human culture, of which language is an integral part.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
