arXiv:2606.30096v1 Announce Type: new 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 speak
The proliferation of advanced large language models necessitates better methods to quantify their communicative processes and understand information flow. This research addresses a fundamental gap in assessing semantic communication dynamics.
Quantifying semantic content propagation within AI communication is critical for developing more reliable, controllable, and interpretable AI agents and systems. This improves our understanding of how information truly flows in natural language contexts.
Our ability to precisely measure and decompose semantic information flow in AI-driven communication is enhanced, enabling more sophisticated analysis of agent interactions and human-AI interfaces. The framework provides new tools for understanding the 'why' and 'how' of language model output.
- · AI researchers
- · Computational linguists
- · AI ethics and safety organizations
- · Developers of opaque black-box AI communication systems
Improved metrics for evaluating the effectiveness and intent of AI communication will be developed.
This could lead to a 'semantic debugging' capability for complex AI agent systems, pinpointing where information is lost or misinterpreted.
More robust and less manipulable AI communication protocols might emerge, influencing the design of future AI-human and AI-AI interactions across sensitive domains.
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Read at arXiv cs.CL