Social Intelligence in the Age of LLM s (2025)

Abstract: With the emergence of Large Language Models (LLMs), we now have unprecedented opportunities to incorporate human-like communication and context-aware interactions into artificial systems. But what is the current state of LLMs’ capability of social interaction? Can they truly understand social scenarios, perform social reasoning, or interact with humans as socially competent agents? We propose this tutorial as an introduction to and an overview of different aspects of artificial social intelligence and their relationship with LLMs. In this tutorial, we will explore these questions by introducing scientific methods for evaluating social intelligence in LLMs, highlighting the key challenges, and identifying promising research directions. Participants will not only gain a comprehensive overview of the field’s progress, but also acquire technical skills on analysing and developing LLM-based social intelligence.

## motivation and objectives with the emergence of large language models (llms), we now have unprecedented opportunities to incorporate human-like communication and context-aware interactions into artificial systems. but what is the current state of llms' capability of social interaction? can they truly understand social scenarios, perform social reasoning, or interact with humans as socially competent agents? we propose this tutorial as an introduction to and an overview of different aspects of artificial social intelligence and their relationship with llms. in this tutorial, we will explore these questions by introducing scientific methods for evaluating social intelligence in llms, highlighting the key challenges, and identifying promising research directions. participants will not only gain a comprehensive overview of the field's progress, but also acquire technical skills on analysing and developing llm-based social intelligence. ## tutorial outline this will be a three-hour tutorial devoted to the cutting-edge topic of social intelligence and large language models, divided into four themes. each theme will take 40 minutes including q&a. we provide a sample tutorial schedule in ## diversity considerations we will make our tutorial materials digitally accessible to all participants. during the tutorial sessions, we will work with student volunteers to encourage open dialogue and promote active listening, allowing participants to share their thoughts and experiences without fear of judgment. after the tutorial, we will actively collect feedback to identify areas for improvement related to diversity and inclusion and share it with future tutorial presenters. our presenter team will share our tutorial with a worldwide audience by promoting it on social media, and to diverse research communities. our presenters include both junior and senior researchers. thus, we have diversified instructors which will also help encourage diverse audience. diyi has experience co-organizing widening nlp workshops at both naacl and acl, and actively works on inviting undergraduate students to research and promoting diversity such as by speaking at ai4all and local high-schools at atlanta. we will work with acl/naacl d&i teams, and consult resources such as the big directory to diversify our audience participation. ## reading list and prerequisite the tutorial is targeted toward nlp researchers and practitioners working with humans. breadth while we will give pointers to dozens of relevant papers over the course of the tutorial, we plan to cover around 15 research papers in close detail. only 4 of the "deep dive" papers will come from the presenter team. ## tutorial details audience size we expect the audience size to be around 100 for a physical conference, and around 150 for a virtual conference. our tutorial will likely bring a similar audience as the nlp+hci workshop ( https://sites.google.com/view/ hciandnlp-2021/home?@xmath0. open access we will put the slides, code, and other teaching materials online for public access, as well as consent to adding the video recording of our tutorial in the acl anthology. ## sharing of tutorial material we plan to make all teaching material available online and agree to allow the publication of slides and video recordings in the acl anthology. ## pedagogical material we plan to do some short hands-on exercises to let the audience try to prompt llms for the social intelligence tasks introduced in this tutorial. ## ethics statement when ai is used in social scenarios, the biases and stereotypes might lead to undesirable consequences. the goal of this tutorial is to offer the audience the tools to evaluate these biases and stereotypes and to mitigate them. we would also introduce latest progress of understanding the risks and safety issues of applying llms in the real world.