Non-referential functions of language such as setting group boundaries, identity construction and regulation of social proximity have rarely found place in the language technology creation process. Nevertheless, their importance has been postulated in literature. While multiple methods to include social information in large language models (LLM) cover group properties (gender, age, geographic relations, professional characteristics), a combination of group social characteristics and individual features of an agent (natural or artificial) play a role in social interaction but have not been studied in generated language. This article explores the orchestration of prompt engineering and retrieval-augmented generation techniques to linguistic features of social proximity and distance in language generated by an LLM. The study uses the immediacy/distance model from literature to analyse language generated by an LLM for different recipients. This research reveals that kinship terms are almost the only way of displaying immediacy in LLM-made conversations.

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person’s expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person’s past expressions, and offer a better understanding of the sentiment from the expresser’s perspective. Additionally, we investigate how a person’s sentiment changes over time so that recent incidents or opinions may have more effect on the person’s current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.

This article describes a model of other-initiated self-repair for a chatbot that helps to practice conversation in a foreign language. The model was developed using a corpus of instant messaging conversations between German native and non-native speakers. Conversation Analysis helped to create computational models from a small number of examples. The model has been validated in an AIML-based chatbot. Unlike typical retrieval-based dialogue systems, the explanations are generated at run-time from a linguistic database.