The paper examines how users’ prompts to large language models evolve over time, applying speech act and politeness theory to a corpus of 2,000 English prompts from 2023 and 2025. It finds a shift toward more indirect, implicit, and fragmentary directive speech acts, with a notable 14.9‑percentage‑point drop in explicit propositional content and a decline in politeness markers. This suggests users increasingly rely on the model’s inferential abilities, treating it as a competent implicature resolver.
By Kristina \v{S}ekrst, Virna Karli\'c
VakyArth is the first pragmatic benchmark for Indic languages, covering Hindi, Punjabi, Tamil, and Malayalam. It tests models on five pragmatic phenomena—deixis, speech acts, implicature, social pragmatics, and coherence—using multiple-choice questions, natural language inference, and translation tasks authored by native speakers. Evaluation of multilingual LLMs shows consistent failures on pragmatic meanings rooted in Indic linguistic and cultural conventions, with systematic differences across languages and tasks.
By Usneek Singh, Poorvaja Veera Balaji Kumar, Parth Nanda, Anand Madhusoodanan, Geyang Guo, Wei Xu, Junyi Jessy L
The paper presents a Japanese discourse parsing system built on Segmented Discourse Representation Theory (SDRT) by translating existing English SDRT corpora with a large language model (LLM). Comparative experiments with prompt-based and fine-tuned LLMs show that Japanese and English parsers achieve similar performance, indicating cross‑lingual applicability of SDRT. Ablation studies reveal that Japanese discourse markers have little influence, while predictions rely mainly on contextual discourse relations rather than clause content, explaining the comparable results across typologically distinct languages.
By Satomu Tomisaki, Yosuke Mikami, Amane Watahiki, Hitomi Yanaka
The paper studies how large language models (LLMs) handle the discourse particles "just" and "only" in natural conversation. Behavioral experiments show that both humans and LLMs prefer these particles and are more sensitive to "just" than "only", while instruction tuning enhances this sensitivity. Mechanistic analysis identifies specific units in the models that respond to each particle, revealing an asymmetric overlap in their internal representations.
By William Sheffield, Jiahui Chen, Amy Zhang, Alex Warstadt, Junyi Jessy Li, Kanishka Misra
arXiv:2603.18007v2 Announce Type: replace-cross
Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to inf...
By Anna Babarczy, Andras Lukacs, Peter Vedres, Zeteny Bujka
RAG‑RST is a retrieval‑augmented framework that supplies a formal discourse schema as in‑context demonstrations for Rhetorical Structure Theory parsing. By retrieving exemplar EDU pairs, it improves nuclearity F1 from 0.794 to 0.846 and relation F1 from 0.738 to 0.788 without any training. Two configurations—precomputed and dynamic—both outperform existing RST parsers, with the dynamic variant consistently strongest, and the approach shows that a formal linguistic schema can be provided to a foundation model as a demonstration rather than encoded in its parameters.
By Mohammadreza Sediqin, Shlomo Engelson Argamon
arXiv:2607. 18570v1 Announce Type: cross Abstract: Discourse relations provide document structure, critical to language understanding and enabling language model performance and ethicality.
By Abhidip Bhattacharyya, Shira Wein
arXiv:2604. 24079v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue.
By Jisoo Yang, Jongwon Ryu, Minuk Ma, Trung X. Pham, Junyeong Kim
arXiv:2606. 01845v1 Announce Type: cross Abstract: Although large language models (LLMs) have shown considerable progress in pragmatic language understanding, prior research has focused mainly on their comprehension of verbal behavior.
By Sugyeong Eo, Heuiseok Lim
arXiv:2604. 02512v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly exhibit human-like patterns of pragmatic and social reasoning.
By Roland M\"uhlenbernd
The article examines chatbots as partners in problem‑solving conversations, arguing that basic chatbots—comprising a large language model (LLM) and a simple interface—are multifaceted but cannot match human cognitive flexibility. Drawing on Aggregation Dynamics, Cognitive Linguistics, Neuropsychology, and Psychology, the authors describe how LLMs encode artificial metaphorical problem propagations from training data, which only partially imitate human thinking. They conclude that further LLM development will not yield true thinking partners, yet chatbots are widely used, making their understanding socially and politically important.
By S. F. M. van Vlijmen, H. D. Lethe jr
arXiv:2609.39225v1 Announce Type: new
Abstract: Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations. Whil...
By Siddharth Bhargava, Sara Tonelli, Patricia Mart\'in-Rodilla, Javier Parapar