Information Dynamics of Language Communication
Introduced STE and SPID frameworks to quantify semantic information flow and multi-source contributions in communication.
Key Findings
Methodology
Proposed Semantic Transfer Entropy (STE) and Semantic Partial Information Decomposition (SPID), leveraging large language models (LLMs) as probabilistic estimators to quantify semantic flow and decompose multi-source contributions into redundant, unique, and synergistic components.
Key Results
- STE detected reduced information flow in cognitively rigid dialogues, with flexible sources showing significantly higher transfer (0.52 vs. 0.46, p<0.001).
- STE revealed persuaders dominate semantic flow, with persuader-to-persuadee transfer averaging 1.70 vs. 0.05 bits/token.
- SPID showed argumentative premises contribute synergistic information, averaging 0.32 bits/token, with redundancy increasing as premise count grows.
Significance
The framework provides a unified tool for studying semantic dynamics in communication, applicable to digital discourse, education, clinical dialogues, and more, addressing gaps in directionality and multi-source analysis.
Technical Contribution
First application of STE and SPID to semantic flow analysis, integrating LLM-based probability estimation and providing theoretical and engineering advancements in multi-source decomposition.
Novelty
Introduced STE and SPID as the first unified framework to quantify semantic flow and multi-source contributions, addressing limitations in existing methods.
Limitations
- STE heavily depends on LLM architecture and quality.
- SPID has high computational complexity for more than four sources.
- Experiments primarily used English datasets; cross-lingual applicability remains untested.
Future Work
Future directions include extending STE and SPID to multilingual datasets and optimizing SPID for higher computational efficiency in multi-source scenarios.
AI Executive Summary
Semantic information flow in communication remains poorly understood. This study introduces two information-theoretic frameworks, Semantic Transfer Entropy (STE) and Semantic Partial Information Decomposition (SPID), leveraging large language models to quantify semantic dynamics and decompose multi-source contributions. Experiments demonstrated STE's ability to detect reduced flow in cognitively rigid dialogues, reveal persuader dominance in persuasion, and distinguish high- vs. low-quality psychotherapy. SPID uncovered synergistic contributions in argumentative premises, showing redundancy increases with premise count. These findings provide new tools for analyzing communication dynamics across diverse domains.
STE quantifies directional semantic influence by masking attention from the target to the source, while SPID decomposes multi-source contributions into redundant, unique, and synergistic components using mutual information metrics. Experiments spanned synthetic dialogues, persuasion corpora, therapeutic sessions, and argumentative essays, validating the framework's theoretical significance and cross-domain applicability.
Despite its advantages, the framework faces limitations such as dependency on model quality and computational complexity. Future research should explore multilingual scenarios and optimize algorithms for larger-scale applications.
Deep Analysis
Background
Research on communication dynamics has evolved from word frequency statistics to distributional semantics and contextual neural representations, yet existing methods fail to simultaneously address directionality and multi-source contributions.
Core Problem
Current approaches lack a unified framework to quantify semantic flow and multi-source contributions, leaving gaps in analyzing complex dialogue scenarios.
Innovation
Introduced STE and SPID frameworks: STE quantifies directional semantic flow, SPID decomposes multi-source contributions into redundant, unique, and synergistic components, leveraging LLMs for probabilistic estimation.
Methodology
- �� STE quantifies semantic flow by masking attention from the target to the source.
- �� SPID uses the minimum mutual information redundancy functional to decompose multi-source contributions.
- �� LLMs serve as probabilistic estimators for semantic dynamics.
Experiments
Experiments included cognitively rigid dialogues, persuasion corpora, therapeutic sessions, and argumentative essays, validating STE and SPID across diverse scenarios.
Results
STE detected reduced flow in rigid dialogues; SPID revealed synergistic contributions in argumentative premises, with redundancy increasing as premise count grows.
Applications
Applicable to digital discourse analysis, educational interaction optimization, and clinical dialogue quality assessment.
Limitations & Outlook
STE depends on model architecture; SPID has high computational complexity; experiments primarily used English datasets, limiting cross-lingual validation.
Plain Language Accessible to non-experts
Imagine chatting with a friend who tells a story, and you respond based on that story. This is what STE measures: the influence of your friend's story on your reply. SPID, on the other hand, analyzes how two friends' stories together shape your response, breaking down which parts are redundant, unique, or synergistic.
ELI14 Explained like you're 14
Think of playing a multiplayer game where your teammates give you tips, and you act based on them. STE measures how much one teammate’s tip influences your actions. SPID is like analyzing how tips from multiple teammates combine to shape your decisions, showing which tips overlap, which are unique, and which work together!
Glossary
Semantic Transfer Entropy
Quantifies directional semantic influence between communicators using information theory.
Used to analyze directional semantic flow in dialogues.
Semantic Partial Information Decomposition
Decomposes multi-source contributions into redundant, unique, and synergistic components.
Used to analyze multi-source contributions to target language.
Redundant Information
Information shared across multiple sources.
Quantified in SPID to measure overlap.
Synergistic Information
Information jointly provided by multiple sources that exceeds individual contributions.
Quantified in SPID to measure collaboration.
Large Language Model
Deep learning-based language models trained on vast text corpora for natural language prediction.
Used as probabilistic estimators in STE and SPID.
Open Questions Unanswered questions from this research
- 1 How can SPID be optimized to support more sources?
- 2 What is the cross-lingual applicability of STE and SPID?
Applications
Immediate Applications
Therapy Quality Assessment
Use STE to analyze therapist-client dialogue dynamics and assess session quality.
Educational Interaction Optimization
Apply SPID to analyze teacher-student interactions and improve teaching strategies.
Long-term Vision
Human-AI Interaction Enhancement
Use STE and SPID to analyze semantic dynamics in human-AI dialogues and improve interaction quality.
Abstract
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.