Liu Qi and Lin You-Jing
Face-to-face conversation is the most fundamental and pervasive form of language use. The success of language generation by large language model (LLM) technology therefore depends crucially on whether it can maximally exhibit the social and communicative properties of human language use. In this regard, interactional linguistics offers more communication-sensitive criteria than traditional linguistic approaches. By rendering tacit regularities of verbal conduct and their underlying interactional mechanisms explicit and empirically testable, it reveals how speakers deploy linguistic resources to accomplish natural social interaction and, by extension, how artificial systems might be designed to do so. In this regard, interactional linguistics offers insights that better profile the nature of communication than traditional linguistic approaches. It uncovers patterns of speech behavior and related interactional mechanisms, revealing how humans manipulate language to achieve natural social interaction, and by extension, how machines should do so. As a matter of fact, some core concepts in interactional linguistics have already been incorporated into the annotation frameworks of AI dialogue systems. TRP (Transition Relevance Place), for one, has been introduced for the prediction of potential turn-ending points, enhancing LLMs’ “interactional awareness” of when to take the floor, thereby enabling smooth turn-taking in human-machine interaction. Moreover, even in the pre-LLM era, AI developers at Google and elsewhere had already figured out that being too fluent is unnatural, and embedded discourse markers and other “disfluencies” into machine language output. In light of the linguistic concepts already adopted by AI developers, this study collects dialogues conducted between large language models and compares them with naturally occurring human conversations. It further examines discourse markers, pauses, elongations, repairs, and other “disfluencies”, as well as turn overlaps arising from TRP-based predictions. The analysis reveals that these linguistic features all carry interactional functions in human speech and exhibit observable regularities, while in LLM language output, they are relatively scarce or even entirely absent. These findings point to two priorities for future research: on the one hand, linguistics studies should place greater emphasis on the recurrent patterns of speech interactional behavior in verbal interaction; on the other hand, greater efforts should be devoted to building multimodal corpora, which will form a solid empirical foundation for language design in artificial systems.
