# Pragmatics beyond humans: meaning, communication, and LLMs

arXiv: https://arxiv.org/abs/2508.06167 · HTML: https://mlsanity.com/paper/2508.06167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 79 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 30 of 30)

- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve](https://mlsanity.com/paper/2309.13638/) (2023)
- [Meaning without reference in large language models](https://mlsanity.com/paper/2208.02957/) (2022)
- [PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities](https://mlsanity.com/paper/2401.07078/) (2024)
- [Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://mlsanity.com/paper/1703.10186/) (2017)
- [Testing AI on language comprehension tasks reveals insensitivity to underlying meaning](https://mlsanity.com/paper/2302.12313/) (2023)
- [On the proper role of linguistically-oriented deep net analysis in linguistic theorizing](https://mlsanity.com/paper/2106.08694/) (2021)
- [Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance](https://mlsanity.com/paper/2402.14531/) (2024)
- [The Vector Grounding Problem](https://mlsanity.com/paper/2304.01481/) (2023)
- [Mission: Impossible Language Models](https://mlsanity.com/paper/2401.06416/) (2024)
- [Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2502.12378/) (2025)
- [Empirical evidence of Large Language Model's influence on human spoken communication](https://mlsanity.com/paper/2409.01754/) (2024)
- [Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection](https://mlsanity.com/paper/2402.11406/) (2024)
- [Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding](https://mlsanity.com/paper/2310.19671/) (2023)
- [Does GPT-4 surpass human performance in linguistic pragmatics?](https://mlsanity.com/paper/2312.09545/) (2023)
- [Evaluating statistical language models as pragmatic reasoners](https://mlsanity.com/paper/2305.01020/) (2023)
- [Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom](https://mlsanity.com/paper/2404.19509/) (2024)
- [An Incremental Iterated Response Model of Pragmatics](https://mlsanity.com/paper/1810.00367/) (2018)
- [Scope Ambiguities in Large Language Models](https://mlsanity.com/paper/2404.04332/) (2024)
- [Assessing the potential of LLM-assisted annotation for corpus-based pragmatics and discourse analysis: The case of apology](https://mlsanity.com/paper/2305.08339/) (2023)
- [Exploring the Word Sense Disambiguation Capabilities of Large Language Models](https://mlsanity.com/paper/2503.08662/) (2025)
- [LLMs Among Us: Generative AI Participating in Digital Discourse](https://mlsanity.com/paper/2402.07940/) (2024)
- [MultiPragEval: Multilingual Pragmatic Evaluation of Large Language Models](https://mlsanity.com/paper/2406.07736/) (2024)
- [Non-literal Understanding of Number Words by Language Models](https://mlsanity.com/paper/2502.06204/) (2025)
- [Are LLMs good pragmatic speakers?](https://mlsanity.com/paper/2411.01562/) (2024)
- [Meaning and understanding in large language models](https://mlsanity.com/paper/2310.17407/) (2023)
- [Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions](https://mlsanity.com/paper/2405.05776/) (2024)
- [Chatting with Bots: AI, Speech Acts, and the Edge of Assertion](https://mlsanity.com/paper/2410.16645/) (2024)
- [AI with Alien Content and Alien Metasemantics](https://mlsanity.com/paper/2405.19808/) (2024)

## 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.