Pragmatics beyond humans: meaning, communication, and LLMs

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, particularly the Rational Speech Act framework, offers a more compatible teleology by focusing on optimization rather than truth-evaluation. The third section addresses the issue of substitutionalism in three forms - generalizing, linguistic, and communicative - highlighting the anthropomorphic biases that distort LLM evaluation and obscure the role of human communicative subjects. Finally, the paper introduces the concept of context frustration to describe the paradox of increased contextual input paired with a collapse in contextual understanding, emphasizing how users are compelled to co-construct pragmatic conditions both for the model and themselves. These arguments suggest that pragmatic theory may need to be adjusted or expanded to better account for communication involving generative AI.