Pragmatics beyond humans: meaning, communication, and LLMs
V\'it Gvo\v{z}diak

TL;DR
This paper redefines pragmatics as a dynamic, socially embedded interface for language, critically examines LLMs' impact on traditional theories, and proposes new frameworks to better understand AI-human communication.
Contribution
It introduces the Human-Machine Communication framework and advocates for probabilistic pragmatics to better align with LLMs' predictive nature.
Findings
LLMs destabilize traditional hierarchies of meaning.
Probabilistic pragmatics aligns better with LLMs than classical theories.
Context frustration highlights the paradox of increased input but decreased understanding.
Abstract
The paper reconceptualizes pragmatics not as a subordinate, third dimension of meaning, but as a dynamic interface through which language operates as a socially embedded tool for action. With the emergence of large language models (LLMs) in communicative contexts, this understanding needs to be further refined and methodologically reconsidered. The first section challenges the traditional semiotic trichotomy, arguing that connectionist LLM architectures destabilize established hierarchies of meaning, and proposes the Human-Machine Communication (HMC) framework as a more suitable alternative. The second section examines the tension between human-centred pragmatic theories and the machine-centred nature of LLMs. While traditional, Gricean-inspired pragmatics continue to dominate, it relies on human-specific assumptions ill-suited to predictive systems like LLMs. Probabilistic pragmatics,…
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Taxonomy
TopicsNatural Language Processing Techniques
