Pragmatics beyond humans: meaning, communication, and LLMs

• URL: http://arxiv.org/abs/2508.06167v1
• Date: Fri, 08 Aug 2025 09:34:41 GMT
• Title: Pragmatics beyond humans: meaning, communication, and LLMs
• Authors: Vít Gvoždiak,
• Abstract summary: The paper reconceptualizes pragmatics as a dynamic interface through which language operates as a tool for action.<n>The paper argues that this understanding needs to be further refined and methodologically reconsidered.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agents [39.49473274097833]
  Anthropomorphisation -- whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise of large language model (LLM)-based conversational agents (CAs)<n>This scoping review maps ethically oriented work on anthropomorphising LLM-based CAs across five databases and three preprint repositories.
  arXiv  Detail & Related papers  (2026-01-14T21:03:11Z)
• On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts [69.69818198773244]
  We study a range of LMs on both language comprehension and language production.<n>We find that state-of-the-art LMs, but not smaller ones, achieve strong performance on language comprehension.
  arXiv  Detail & Related papers  (2025-09-08T17:59:32Z)
• The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning [2.0800882594868293]
  Unified Cognitive Consciousness Theory (UCCT) casts them as vast unconscious pattern repositories.<n>UCCT formalizes this process as Bayesian competition between statistical priors learned in pre-training and context-driven target patterns.<n>We ground the theory in three principles: threshold crossing, modality, and density-distance predictive power.
  arXiv  Detail & Related papers  (2025-06-02T18:12:43Z)
• The Pragmatic Mind of Machines: Tracing the Emergence of Pragmatic Competence in Large Language Models [6.187227278086245]
  Large language models (LLMs) have demonstrated emerging capabilities in social intelligence, including implicature resolution and theory-of-mind reasoning.<n>In this work, we evaluate whether LLMs at different training stages can accurately infer speaker intentions.<n>We systematically evaluate 22 LLMs across 3 key training stages: after pre-training, supervised fine-tuning (SFT), and preference optimization.
  arXiv  Detail & Related papers  (2025-05-24T04:24:59Z)
• From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning [63.25540801694765]
  Large Language Models (LLMs) demonstrate striking linguistic abilities, yet whether they achieve this same balance remains unclear.<n>We apply the Information Bottleneck principle to quantitatively compare how LLMs and humans navigate this compression-meaning trade-off.
  arXiv  Detail & Related papers  (2025-05-21T16:29:00Z)
• Human-like conceptual representations emerge from language prediction [72.5875173689788]
  Large language models (LLMs) trained exclusively through next-token prediction over language data exhibit remarkably human-like behaviors.<n>Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized?<n>Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts.<n>These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding.
  arXiv  Detail & Related papers  (2025-01-21T23:54:17Z)
• Do Large Language Models Advocate for Inferentialism? [0.0]
  The emergence of large language models (LLMs) such as ChatGPT and Claude presents new challenges for philosophy of language.<n>This paper explores Robert Brandom's inferential semantics as an alternative foundational framework for understanding these systems.
  arXiv  Detail & Related papers  (2024-12-19T03:48:40Z)
• Language Models as Semiotic Machines: Reconceptualizing AI Language Systems through Structuralist and Post-Structuralist Theories of Language [0.0]
  This paper proposes a novel framework for understanding large language models (LLMs) I argue that LLMs should be understood as models of language itself, aligning with Jacques's concept of 'writing' (l'ecriture) I apply Saussure's critique of Saussure to position 'writing' as the object modeled by LLMs, offering a view of the machine's'mind' as a statistical approximation of sign behavior.
  arXiv  Detail & Related papers  (2024-10-16T21:45:54Z)
• Regularized Conventions: Equilibrium Computation as a Model of Pragmatic Reasoning [72.21876989058858]
  We present a model of pragmatic language understanding, where utterances are produced and understood by searching for regularized equilibria of signaling games. In this model speakers and listeners search for contextually appropriate utterance--meaning mappings that are both close to game-theoretically optimal conventions and close to a shared, ''default'' semantics.
  arXiv  Detail & Related papers  (2023-11-16T09:42:36Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• Emergence of Pragmatics from Referential Game between Theory of Mind Agents [64.25696237463397]
  We propose an algorithm, using which agents can spontaneously learn the ability to "read between lines" without any explicit hand-designed rules. We integrate the theory of mind (ToM) in a cooperative multi-agent pedagogical situation and propose an adaptive reinforcement learning (RL) algorithm to develop a communication protocol.
  arXiv  Detail & Related papers  (2020-01-21T19:37:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.