Our approach requires no additional training and enables highly flexible module composition.

However,existingmodelsand benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping byalleviating task-specific engineering effortsandby providing users with interactiveconversations.

Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge--situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning.

However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers.