分析大模型生成的剧本游戏文本，发现其语言风格独特。
Collaborative Storytelling and LLM: A Linguistic Analysis of Automatically-Generated Role-Playing Game Sessions
- 对比大模型、真人对话、剧本和书籍的语法词汇特征
- 大模型生成内容语言模式明显区别于各类文本
- 揭示训练数据如何影响模型叙事表达方式
角色扮演游戏（RPG）是玩家通过互动共同创造叙事的新兴形式，主要以口头交流为主，正受到越来越多关注。许多研究尝试将大语言模型（LLM）作为游戏中的角色参与。本文旨在探究当要求大模型在无人干预下生成完整游戏会话时，其语言是否呈现口语化或书面化特征。我们对生成文本的词汇与句法特征进行语言学分析，并与真实对话、真人游戏记录及书籍文本对比。结果表明，大模型生成的内容展现出与其他文本类别均不同的语言模式，包括口语对话、人类主导的RPG会话以及书籍文本。该分析揭示了训练数据如何塑造大模型的表达方式，并为理解其叙事能力提供了重要线索。
原文摘要 · Abstract (English)
Role-playing games (RPG) are games in which players interact with one another to create narratives. The role of players in the RPG is largely based on the interaction between players and their characters. This emerging form of shared narrative, primarily oral, is receiving increasing attention. In particular, many authors investigated the use of an LLM as an actor in the game. In this paper, we aim to discover to what extent the language of Large Language Models (LLMs) exhibit oral or written features when asked to generate an RPG session without human interference. We will conduct a linguistic analysis of the lexical and syntactic features of the generated texts and compare the results with analyses of conversations, transcripts of human RPG sessions, and books. We found that LLMs exhibit a pattern that is distinct from all other text categories, including oral conversations, human RPG sessions and books. Our analysis has shown how training influences the way LLMs express themselves and provides important indications of the narrative capabilities of these tools.
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