User-in-the-Loop Evaluation of Multimodal LLMs for Activity Assistance

Mrinal Verghese, Brian Chen, Hamid Eghbalzadeh, Tushar Nagarajan, Ruta P Desai; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1144-1154

Our research investigates the capability of modern multimodal reasoning models powered by Large Language Models (LLMs) to facilitate vision-powered assistants for multi-step daily activities. Such assistants must be able to 1) encode relevant visual history from the assistant's sensors e.g. camera 2) forecast future actions for accomplishing the activity and 3) replan based on the user in the loop. To evaluate the first two capabilities grounding visual history and forecasting in short and long horizons we conduct benchmarking of two prominent classes of multimodal LLM approaches - Socratic Models and Vision Conditioned Language Models (VCLMs) on video-based action anticipation tasks using offline datasets. These offline benchmarks however do not allow us to close the loop with the user which is essential to evaluate the replanning capabilities and measure successful activity completion in assistive scenarios. To that end we conduct a first-of-its-kind user study with 18 participants performing 3 different multi-step cooking activities while wearing an egocentric observation device called Aria and following assistance from multimodal LLMs. We find that the Socratic approach outperforms VCLMs in both offline and online settings. We further highlight how grounding long visual history common in activity assistance remains challenging in current models especially for VCLMs and demonstrate that offline metrics do not indicate online performance.