Aller directement au menu principal Aller directement au contenu principal Aller au pied de page
Językoznawstwo baner

No 3/28 (2026)

Artykuły

Communicative convergence in human–LLM written dialogues: A review of linguistic adaptation in interactions with large language models

DOI: https://doi.org/10.25312/j.10781  [Google Scholar]
Publiée: 2026-10-23

Résumé

This review article provides a comparative analysis of communicative convergence in written dialogues between humans and Large Language Models (LLMs). It draws on theoretical work on communication accommodation and interactive alignment as well as empirical research on linguistic adaptation in human–computer and human–LLM interaction. The reviewed studies address lexical, syntactic, stylistic, pragmatic, and ideological alignment, together with the effects of prompt wording and dialogue history on model responses. The comparison shows that similar manifestations of convergence arise from different mechanisms: in humans, they may reflect affiliation, priming, and interactional coordination, whereas in LLMs they result from probabilistic text generation and sensitivity to context. Convergence is bidirectional but asymmetrical: users adapt their language to the perceived characteristics and constraints of the system, while models reproduce or amplify cues present in user input. Apparent alignment should not be treated as evidence of communicative intention or shared understanding. Instead, human–LLM convergence is best understood as an interactional effect jointly produced by human social-cognitive mechanisms and the model’s contextual responsiveness. The review also identifies a shortage of longitudinal research and studies involving morphologically rich languages, including Polish.

Références

  1. Amodei D., Olah C., Steinhardt J., Christiano P., Schulman J., Mané D. (2016), Concrete problems in AI safety, “arXiv”, https://doi.org/10.48550/arXiv.1606.06565 [Google Scholar]
  2. Bender E.M., Gebru T., McMillan-Major A., Shmitchell S. (2021), On the dangers of stochastic parrots: Can language models be too big?, [in:] FAccT ‘21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, New York: Association for Computing Machinery, pp. 610–623, https://doi.org/10.1145/3442188.3445922 [Google Scholar]
  3. Bommasani R. et al. (2021), On the opportunities and risks of foundation models, “arXiv”, https://doi.org/10.48550/arXiv.2108.07258 [Google Scholar]
  4. Branigan H.P., Pickering M.J., Cleland A.A. (2000), Syntactic co-ordination in dialogue, “Cognition”, vol. 75(2), pp. B13–B25, https://doi.org/10.1016/S0010-0277(99)00081-5 [Google Scholar]
  5. Brennan S.E., Clark H.H. (1996), Conceptual pacts and lexical choice in conversation, “Journal of Experimental Psychology: Learning, Memory, and Cognition”, vol. 22(6), pp. 1482–1493, https://doi.org/10.1037/0278-7393.22.6.1482 [Google Scholar]
  6. Brown T.B. et al. (2020), Language models are few-shot learners, “arXiv”, https://doi.org/10.48550/arXiv.2005.14165 [Google Scholar]
  7. Chowdhery A. et al. (2022), PaLM: Scaling language modeling with Pathways, “arXiv”, https://doi.org/10.48550/arXiv.2204.02311 [Google Scholar]
  8. Christiano P., Leike J., Brown T.B., Martic M., Legg S., Amodei D. (2017), Deep reinforcement learning from human preferences, “arXiv”, https://doi.org/10.48550/arXiv.1706.03741 [Google Scholar]
  9. Chung H.W. et al. (2022), Scaling instruction-finetuned language models, “arXiv”, https://doi.org/10.48550/arXiv.2210.11416 [Google Scholar]
  10. Cowan B.R., Branigan H.P., Obregón M., Bugis E., Beale R. (2015), Voice anthropomorphism, interlocutor modelling and alignment effects on syntactic choices in human–computer dialogue, “International Journal of Human-Computer Studies”, vol. 83, pp. 27–42, https://doi.org/10.1016/j.ijhcs.2015.05.008 [Google Scholar]
  11. Danescu-Niculescu-Mizil C., Gamon M., Dumais S. (2011), Mark my words!: Linguistic style accommodation in social media, [in:] S. Srinivasan, K. Ramamritham, A. Kumar, M.P. Ravindra, E. Bertino, R. Kumar (eds.), Proceedings of the 20th International Conference on World Wide Web, New York: Association for Computing Machinery, pp. 745–754, https://doi.org/10.1145/1963405.1963509 [Google Scholar]
  12. Devlin J., Chang M.-W., Lee K., Toutanova K. (2018), BERT: Pre-training of deep bidirectional transformers for language understanding, “arXiv”, https://doi.org/10.48550/arXiv.1810.04805 [Google Scholar]
  13. French D., D’Mello S., von der Wense K. (2024), Aligning to adults is easy, aligning to children is hard: A study of linguistic alignment in dialogue systems, [in:] N. Soni, L. Flek, A. Sharma, D. Yang, S. Hooker, H.A. Schwartz (eds.), Proceedings of the 1st Human-Centered Large Language Modeling Workshop, Kerrville: Association for Computational Linguistic, pp. 81–87, https://doi.org/10.18653/v1/2024.hucllm-1.7 [Google Scholar]
  14. Garrod S., Pickering M.J. (2009), Joint action, interactive alignment, and dialog, “Topics in Cognitive Science”, vol. 1(2), pp. 292–304, https://doi.org/10.1111/j.1756-8765.2009.01020.x [Google Scholar]
  15. Giles H., Coupland N., Coupland J. (1991), Accommodation theory: Communication, context, and consequence, [in:] H. Giles, J. Coupland, N. Coupland (eds.), Contexts of accommodation, Cambridge: Cambridge University Press, pp. 1–68, https://doi.org/10.1017/CBO9780511663673.001 [Google Scholar]
  16. Giles H., Ogay T. (2007), Communication accommodation theory, [in:] B.B. Whaley., W. Samter (eds.), Explaining communication: Contemporary theories and exemplars, Mahwah, NJ: Lawrence Erlbaum Associates, pp. 293–310. [Google Scholar]
  17. Goot van der R., Sharaf I., Imankulova A., Üstün A., Stepanović M., Ramponi A., Khairun¬nisa S.O., Komachi M., Plank B. (2021), From masked language modeling to translation: Non-English auxiliary tasks improve zero-shot spoken language understanding, [in:] K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, Y. Zhou (eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg: Association for Computational Linguistics, pp. 2479–2497, https://doi.org/10.18653/v1/2021.naacl-main.197 [Google Scholar]
  18. Harris Z.S. (1954), Distributional structure, “Word”, vol. 10(2–3), pp. 146–162, https://doi.org/10.1080/00437956.1954.11659520 [Google Scholar]
  19. Healey P.G.T., Purver M., Howes C. (2014), Divergence in dialogue, “PLoS ONE”, vol. 9(6), article e98598, https://doi.org/10.1371/journal.pone.0098598 [Google Scholar]
  20. Hochreiter S., Schmidhuber J. (1997), Long short-term memory, “Neural Computation”, vol. 9(8), pp. 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735 [Google Scholar]
  21. Hoegen R., Aneja D., McDuff D., Czerwinski M. (2019), An end-to-end conversational style matching agent, [in:] Gebhard P., Schneeberger T., Mehlmann G., Baur T., André E. (eds.), Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents, New York: ACM, pp. 111–118, https://doi.org/10.1145/3308532.3329473 [Google Scholar]
  22. Horton W.S. (2017), Theories and approaches to the study of conversation and interactive discourse, [in:] M.F. Schober, D.N. Rapp, M.A. Britt (eds.), The Routledge handbook of discourse processes, London: Routledge, pp. 22–68, https://doi.org/10.4324/9781315687384-3 [Google Scholar]
  23. Ji J. et al. (2025), AI alignment: A comprehensive survey, “arXiv”, https://doi.org/10.48550/ arXiv.2310.19852 [Google Scholar]
  24. Jurafsky D., Martin J.H. (2009), Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition, 2nd ed., Upper Saddle River, NJ: Pearson Prentice Hall. [Google Scholar]
  25. Kaplan J., McCandlish S., Henighan T., Brown T.B., Chess B., Child R., Gray S., Radford A., Wu J., Amodei D. (2020), Scaling laws for neural language models, “arXiv”, https://doi.org/10.48550/arXiv.2001.08361 [Google Scholar]
  26. Kostić B., Fallon C., Risch J., Löser A. (2026), Same meaning, different scores: Lexical and syntactic sensitivity in LLM evaluation, “arXiv”, https://doi.org/10.48550/arXiv.2602.17316 [Google Scholar]
  27. Li S. (2010), The effectiveness of corrective feedback in SLA: A meta-analysis, “Language Learning”, vol. 60(2), pp. 309–365, https://doi.org/10.1111/j.1467-9922.2010.00561.x [Google Scholar]
  28. Lubold N., Walker E., Pon-Barry H. (2021), Effects of adapting to user pitch on rapport perception, behavior, and state with a social robotic learning companion, “User Modelling and User-Adapted Interaction”, vol. 31(1), pp. 35–73, https://doi.org/10.1007/s11257-020-09267-3 [Google Scholar]
  29. Mikolov T., Chen K., Corrado G., Dean J. (2013), Efficient estimation of word representations in vector space, “arXiv”, https://doi.org/10.48550/arXiv.1301.3781 [Google Scholar]
  30. Mizumoto A. (2025), Automated analysis of common errors in L2 learner production: Prototype web application development, “Studies in Second Language Acquisition”, vol. 47(3), pp. 867–884, https://doi.org/10.1017/S0272263125100934 [Google Scholar]
  31. Nass C., Lee K.M. (2000), Does computer-generated speech manifest personality? An experimental test of similarity-attraction, [in:] T. Turner, G. Szwillus (eds.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’00), New York: Association for Computing Machinery, pp. 329–336, https://doi.org/10.1145/332040.332452 [Google Scholar]
  32. Nass C., Moon Y. (2000), Machines and mindlessness: Social responses to computers, “Journal of Social Issues”, vol. 56(1), pp. 81–103, https://doi.org/10.1111/0022-4537.00153 [Google Scholar]
  33. Ouyang L. et al. (2022), Training language models to follow instructions with human feedback, “arXiv”, https://doi.org/10.48550/arXiv.2203.02155 [Google Scholar]
  34. Park J.S., O’Brien J., Cai C.J., Morris M.R., Liang P., Bernstein M.S. (2023), Generative agents: Interactive simulacra of human behavior, [in:] S. Follmer, J. Han, J. Steimle, N. Henry Riche (eds.), Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23), New York: Association for Computing Machinery, pp. 1–22, https://doi.org/10.1145/3586183.3606763 [Google Scholar]
  35. Pickering M.J., Garrod S. (2004), Toward a mechanistic psychology of dialogue, “Behavioral and Brain Sciences”, vol. 27(2), pp. 169–190, https://doi.org/10.1017/S0140525X04000056 [Google Scholar]
  36. Santurkar S., Durmus E., Ladhak F., Lee C., Liang P., Hashimoto T. (2023), Whose opinions do language models reflect?, “arXiv”, https://doi.org/10.48550/arXiv.2303.17548 [Google Scholar]
  37. Shen H. et al. (2025), Position: Towards bidirectional human-AI alignment, “arXiv”, https://doi.org/10.48550/arXiv.2406.09264 [Google Scholar]
  38. Srivastava A. et al. (2023), Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, “arXiv”, https://doi.org/10.48550/arXiv.2206.04615 [Google Scholar]
  39. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ł., Polosukhin I. (2017), Attention is all you need, [in:] I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S.V.N. Vishwanathan, R. Garnett (eds.), Advances in Neural Information Processing Systems 30, New York: Curran Associates, Inc., pp. 5998–6008. [Google Scholar]

Téléchargements

Les données relatives au téléchargement ne sont pas encore disponibles.

Articles les plus lus par le même auteur ou la même autrice