NL Seminar-InterIntent Investigating Social Intelligence of LLMs via Intention Understanding in a Game context

Speaker: Ziyi Liu, USC

Talk Title: InterIntent Investigating Social Intelligence of LLMs via Intention Understanding in a Game Context

Abstract: REMINDER: Meeting hosts only admit on-line guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please inform us at (nlg-seminar-host(at)isi.edu) to make us aware of your attendance so we can admit you. Specify if you will attend remotely or in person at least one business day prior to the event  Provide your: full name, job title and professional affiliation and arrive at least 10 minutes before the seminar begins. If you do not have access to the 6th Floor for in-person attendance, please check in at the 10th floor main reception desk to register as a visitor and someone will escort you to the conference room location. Zoom Info: https://usc.zoom.us/j/95325436571?pwd=NMJIFIQNQ01esvL9UffxxIp4dnSCmF.1Meeting ID: 953 2543 6571/Passcode: 985321 Abstract: Large language models (LLMs) have demonstrated the potential to mimic human social intelligence. However, most studies focus on simplistic and static self-report or performance-based tests, which limits the depth and validity of the analysis. In this paper, we developed a novel framework, INTERINTENT, to assess LLMs’ social intelligence by mapping their ability to understand and manage intentions in a game setting. We focus on four dimensions of social intelligence: situational awareness, self-regulation, self-awareness, and theory of mind. Each dimension is linked to a specific game task: intention selection, intention following, intention summarization, and intention guessing. Our findings indicate that while LLMs exhibit high proficiency in selecting intentions, achieving an accuracy of 88%, their ability to infer the intentions of others is significantly weaker, trailing human performance by 20%. Additionally, game performance correlates with intention understanding, highlighting the importance of the four components towards success in this game. These findings underline the crucial role of intention understanding in evaluating LLMs’ social intelligence and highlight the potential of using social deduction games as a complex testbed to enhance LLM evaluation. INTERINTENT contributes a structured approach to bridging the evaluation gap in social intelligence within multiplayer games.     

Biography: Ziyi Liu is a second-year PhD student at the University of Southern California, advised by Professor Jieyu Zhao in LIME Lab. Previously, she earned her master’s degree at USC and was a Research Assistant in USC ISI’s Ink Lab for two years under the guidance of Professor Xiang Ren.  Her research focuses on social intelligence and hallucination detection in human-LLM interactions, particularly in evaluating LLM behaviors and aligning LLM values with those of humans. Her work is driven by two key questions: (1) How can we make interactions between models and humans more seamless? (2) How can we ensure the faithfulness of LLMs and avoid hallucinations during interactions? 

Host: Jonathan May and Katy Felkner

More Info: https://www.isi.edu/research-groups-nlg/nlg-seminars/

Webcast: https://www.youtube.com/watch?v=yHfeHKahMoI

Location: Information Science Institute (ISI) - Conf Rm#689

WebCast Link: https://www.youtube.com/watch?v=yHfeHKahMoI

Audiences: Everyone Is Invited

Contact: Pete Zamar

Event Link: https://www.isi.edu/research-groups-nlg/nlg-seminars/