STYLE VARIATION AND POLITENESS STRATEGIES IN LARGE LANGUAGE MODEL-BASED CHATBOTS

Human social existence relies heavily on pragmatics. Consequently, failure to understand certain communicative features leads to unsuccessful interactions, as interlocutors' communicative needs remain unmet, especially in the digital age, where communication occurs through or with machines. This study, therefore, investigated key pragmatic aspects of the language use of selected LLM-based chatbots, including how they vary their language style across prompts and contexts, the consistency of their politeness strategies, and the influence of prompt genre on stylistic features. Grounded in the Speech Adaptation in Human–Computer Interaction theory, the study employed a comparative qualitative method to analyze 36 purposively stratified screenshots from five notable LLM-based chatbots. The results show that the chatbots differ in sentence length, phrasing, formality, prompt adaptation, humour, human simulation, idioms, and structural signposting, as well as in the frequency of contractions and passive constructions. The study also revealed that the chatbots consistently respond to face-threatening acts with respect, empathy, self-criticism, and willingness to cooperate. Significant findings include: Perplexity has the lowest frequency of contractions and least human simulation; Claude produces the longest responses; only ChatGPT withholds silence, shows the highest adherence to clear prompts, and cannot tell time; Gemini is the least versatile stylistically; and Copilot employs more semiotic devices but cannot generate specific APA 7th edition references using Digital Object Identifiers.