Modelling Social Intentions in Complex Conversational Setings

Ivan Kondyurin*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Interpreting and managing social interactions is vital for social well-being, yet existing technologies fall short, particularly in group settings. This research aims to develop advanced machine perception systems for Social Signal Processing to accurately model human social behavior. Our multi-modal generative model aims to integrate multi-modal sensory data input data, contextual information and subjective observers’ narratives, utilizing them as complex input to an adapted Large Language Model, and producing plausible narratives that refect various human perspectives. This human-centered approach leverages both low-level cues and high-order events, ensuring adaptability to diverse observers and contexts. The model’s potential areas of application include cross-cultural interactions, social group integration, and professional meetings, enhancing social harmony and productivity.

Original languageEnglish
Title of host publicationICMI 2024 - Proceedings of the 26th International Conference on Multimodal Interaction
PublisherAssociation for Computing Machinery (ACM)
Pages622-626
Number of pages5
ISBN (Electronic)9798400704628
DOIs
Publication statusPublished - 2024
Event26th International Conference on Multimodal Interaction, ICMI 2024: ICMI 2024 - San Jose, Costa Rica
Duration: 4 Nov 20248 Nov 2024
https://icmi.acm.org/2024/

Publication series

NameACM International Conference Proceeding Series

Conference

Conference26th International Conference on Multimodal Interaction, ICMI 2024
Country/TerritoryCosta Rica
CitySan Jose
Period4/11/248/11/24
Internet address

Keywords

  • Human-centered computing
  • Large Language Models
  • Social Signal Processing

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