Title: Quantifying Human-AI Synergy
Primary area: datasets and benchmarks
Keywords: Interactive Evaluation, Adaptive Benchmarks, Large Language Models (LLMs), Bayesian Item Response Theory, Human-AI Collaboration, Theory of Mind

Abstract: We introduce a novel Bayesian Item Response Theory framework to quantify human–AI synergy, separating individual and collaborative ability while controlling for task difficulty in interactive settings. Unlike standard static benchmarks, our approach models human–AI performance as a joint process, capturing both user-specific factors and moment-to-moment fluctuations. We validate the framework by applying it to human–AI benchmark data (n=667) and find significant synergy. We demonstrate that collaboration ability is distinct from individual problem-solving ability. Users better able to infer and adapt to others’ perspectives achieve superior collaborative performance with AI–but not when working alone. Moreover, moment-to-moment fluctuations in perspective taking influence AI response quality, highlighting the role of dynamic user factors in collaboration. By introducing a principled framework to analyze data from human-AI collaboration, interactive benchmarks can better complement current single-task benchmarks and crowd-assessment methods. This work informs the design and training of language models that transcend static prompt benchmarks to achieve adaptive, socially aware collaboration with diverse and dynamic human partners.

TL;DR: Bayesian Item Response Theory framework that models human–AI collaboration as a dynamic, probabilistic process, separating individual ability, collaborative ability, and task difficulty.