Dialogue as Autocommunication - On Interactions with Large Language Models

Technology and Language. 2024. Vol. 5. No. 2. P. 57–66.

Kartasheva Anna

In a dialog with large language models (LLM) there is a coincidence of the addressee and addressee of the
message, so such a dialog can be called autocommunication. A neural network can only answer a question
that has a formulation. The question is formulated by the one who asks it, i.e. a human being. Human
activity in dialog with neural networks provokes thoughts about the nature of such dialog. Composing
prompts is one of the most creative parts of dialog with neural networks. But it is worth noting that a neural
network is often better at composing prompts than a human. Does this mean that humans need to develop
their questioning skills? In LLM-based dialog systems, the main value to the user is the ability to clarify
and structure their own thoughts. The structuring of thoughts happens through questioning, through
formulating and clarifying questions. Asking the right question is practically answering that question. Thus,
thanks to autocommunication, the development, transformation, and restructuring of the human "I" itself
takes place. Dialogue with large linguistic models acts as a discursive practice that allows people to
formulate their own thoughts and transform their self through autocommunication. It is worth noting that
for this kind of dialog, a certain image of the audience is normative or determinative of the material that
can be produced in response to a given question. This is because the data for model training is provided by
people, even if they do not and have never thought about it. Thus, a dialogic relationship develops between
the generated text and the questioning audience that develops all participants in the communication

Keywords: искусственный интеллект, коммуникация, communication, авторство, диалог, dialogue, authorship, autocommunication, аутокоммуникация, artificial intelligence, Large Language Models, LLM

Publication based on the results of: Development of network analysis in Russia: adaptation of theoretical and methodological approaches and practical application (2024)