Can LLMs be interpersonal without being “in person”? Comparing Attitudinal and Engagement manifestations between human interpreters’ renditions and GPT-5’s translations of political discourse from conference settings

While interpreter-mediated political discourse has attracted growing scholarly attention, comparative studies scrutinising the interpersonal reconstruction of such discourse by human interpreters and large language models (LLMs) remain sparse. This exploratory study investigates how interpersonal meaning is reconstructed in the Chinese–English renditions of Chinese speakers’ speeches delivered at the Boao Forum for Asia (2012–2024) by human interpreters and GPT-5. Drawing on corpus-assisted critical discourse analysis grounded in Appraisal Theory, the study compares human interpreters’ renditions with GPT-5’s translations to uncover different patterns of Attitudinal and Engagement manifestations. The analysis reveals that human interpreters, compared with GPT-5, demonstrate a refined sensitivity to relational cues for instance by reconfiguring the source discourse through evaluative amplification, dialogic expansion and deictic pronouns for solidarity discourse; they not only convey meaning but also perform interpersonal relationships. By contrast, the translations generated by GPT-5 tend to flatten evaluative nuance and interpersonal intent, leaving discourse relationally diminished. Focusing on interpersonal meaning as the site of human mediation, this study illuminates the enduring added value of human interpreters in enacting interpersonal and communicative attunement beyond algorithmic fluency.