Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses

TL;DR

ExploreLLM enhances user interaction with large language models by enabling structured thinking and personalized responses, improving exploratory tasks and user control beyond traditional chatbots.

Contribution

It introduces ExploreLLM, a novel framework that combines natural language and graphical interfaces to facilitate structured exploration and personalization in LLM interactions.

Findings

Users find ExploreLLM helpful for planning and exploration.

ExploreLLM enables easier personalization with high-level preferences.

The approach supports complex user tasks beyond simple chatbots.

Abstract

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.