用信息论量化对话中意义的流动方向与协同作用。
Information Dynamics of Language Communication
- 基于大模型计算语义转移熵与部分信息分解。
- 发现说服者主导话语、治疗对话有方向性差异。
- 适合研究对话结构的学者与临床对话分析者。
在计算语言学中，量化意义在交流中的传播仍不充分。本文提出一种信息论框架，可量化对话者之间语义内容的定向流动，并将多源贡献分解为冗余、独占和协同三类成分。该方法利用大语言模型作为自然语言的概率估计器，计算两个指标：语义转移熵（STE），捕捉说话人之间的定向预测影响；语义部分信息分解（SPID），解析多个来源如何联合塑造目标语言。在四项实验中，该框架检测到认知僵化对话中信息流减弱，揭示了说服者在话语构建中的主导作用，通过治疗师-患者交流的方向性差异区分高/低质量心理治疗，且在论证性文章中识别出前提间的协同贡献。该框架为数字话语、教学互动、临床对话等领域的信息动态研究开辟新路径。
原文摘要 · Abstract (English)
Quantifying how meaning propagates through communicative exchanges remains underdeveloped in computational linguistics. Here we introduce an information-theoretic framework that quantifies the directed flow of semantic content between interlocutors and decomposes multi-source contributions into redundant, unique, and synergistic components. Our approach leverages large language models as probabilistic estimators of natural language to compute two measures: semantic transfer entropy (STE), which captures directed predictive influence between speakers, and semantic partial information decomposition (SPID), which resolves how multiple sources jointly shape a target's language. Across four experiments we show that the framework detects reduced information flow in cognitively rigid dialogue, captures the dominant role of persuaders in shaping discourse, distinguishes high- from low-quality psychotherapy by the directionality of therapist-client information exchange, and reveals synergistic premise contributions in argumentative essays. This framework opens new avenues for studying information dynamics in digital discourse, pedagogical interactions, clinical dialogues, and any domain in which the structure of linguistic exchange is of research relevance.
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