Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized
  Model Responses
Xiao Ma Swaroop Mishra Ariel Liu Sophie Su Jilin Chen Chinmay Kulkarni Heng-Tze Cheng Quoc Le Ed Chi
Large language model (LLM) powered chatbots are primarily text-based today,
and impose a large interactional cognitive load, especially for exploratory or
sensemaking tasks such as planning a trip or learning about a new city. Because
the interaction is textual, users have little scaffolding in the way of
structure, informational "scent", or ability to specify high-level preferences
or goals. We introduce ExploreLLM that allows users to structure thoughts, help
explore different options, navigate through the choices and recommendations,
and to more easily steer models to generate more personalized responses. We
conduct a user study and show that users find it helpful to use ExploreLLM for
exploratory or planning tasks, because it provides a useful schema-like
structure to the task, and guides users in planning. The study also suggests
that users can more easily personalize responses with high-level preferences
with ExploreLLM. Together, ExploreLLM points to a future where users interact
with LLMs beyond the form of chatbots, and instead designed to support complex
user tasks with a tighter integration between natural language and graphical
user interfaces.