Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized
  Model Responses
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.
656d35129496f21be8a06ebd
Xiao Ma
656d35129496f21be8a06ebe
Swaroop Mishra
admin_assigned
2023-12-04T10:16:25.079Z
61841feb7c380c66d40cad54
/avatars/7a79362a24b844860781fb0625527eae.svg
Swaroop Mishra
user
swaroop7
656d35129496f21be8a06ebf
Ariel Liu
656d35129496f21be8a06ec0
Sophie Su
656d35129496f21be8a06ec1
Jilin Chen
656d35129496f21be8a06ec2
Chinmay Kulkarni
admin_assigned
2023-12-04T10:17:24.764Z
64fee57c2fa894e433e7d080
/avatars/dd5140e40da4082a60f81a3d7d3db308.svg
Chinmay Kulkarni
user
kchinmay
656d35129496f21be8a06ec3
Heng-Tze Cheng
656d35129496f21be8a06ec4
Quoc Le
656d35129496f21be8a06ec5
Ed Chi
Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized
  Model Responses
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.
ExploreLLM facilitates exploratory tasks and personalization by integrating natural language with structured interfaces in LLM-powered chatbots.
large language model LLM chatbots exploratory tasks informational scent schema-like structure graphical user interfaces