Pragmatics beyond humans: meaning, communication, and LLMs
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 30 of its 79 references that are in the map. First posted on arXiv 8 Aug 2025.
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30 of 79 references.
References · 30
- 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 616
- 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 386
- 2023Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solvecited by 175
- 2022Meaning without reference in large language modelscited by 76
- 2024PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilitiescited by 58
- 2017Colors in Context: A Pragmatic Neural Model for Grounded Language Understandingcited by 57
- 2023Testing AI on language comprehension tasks reveals insensitivity to underlying meaningcited by 45
- 2021On the proper role of linguistically-oriented deep net analysis in linguistic theorizingcited by 43
- 2024Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performancecited by 41
- 2023The Vector Grounding Problemcited by 39
- 2024Mission: Impossible Language Modelscited by 31
- 2025Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challengescited by 31
18 more
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- 2023Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understandingcited by 20
- 2023Does GPT-4 surpass human performance in linguistic pragmatics?cited by 16
- 2024Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcomcited by 15
- 2023Evaluating statistical language models as pragmatic reasonerscited by 14
- 2018An Incremental Iterated Response Model of Pragmaticscited by 11
- 2024Scope Ambiguities in Large Language Modelscited by 11
- 2023Assessing the potential of LLM-assisted annotation for corpus-based pragmatics and discourse analysis: The case of apologycited by 10
- 2025Exploring the Word Sense Disambiguation Capabilities of Large Language Modelscited by 10
- 2024LLMs Among Us: Generative AI Participating in Digital Discoursecited by 9
- 2024MultiPragEval: Multilingual Pragmatic Evaluation of Large Language Modelscited by 9
- 2025Non-literal Understanding of Number Words by Language Modelscited by 9
- 2024Are LLMs good pragmatic speakers?cited by 7
- 2023Meaning and understanding in large language modelscited by 5
- 2024Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctionscited by 5
- 2024Chatting with Bots: AI, Speech Acts, and the Edge of Assertioncited by 2
- 2024AI with Alien Content and Alien Metasemanticscited by 1
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