title: Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized
    Model Responses
summary: 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.
paper_authors._id: 656d35129496f21be8a06ebd
paper_authors.name: Xiao Ma
paper_authors._id: 656d35129496f21be8a06ebe
paper_authors.name: Swaroop Mishra
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T10:16:25.079Z
paper_authors._id: 61841feb7c380c66d40cad54
paper_authors.avatarUrl: /avatars/7a79362a24b844860781fb0625527eae.svg
paper_authors.fullname: Swaroop Mishra
paper_authors.type: user
paper_authors.user: swaroop7
paper_authors._id: 656d35129496f21be8a06ebf
paper_authors.name: Ariel Liu
paper_authors._id: 656d35129496f21be8a06ec0
paper_authors.name: Sophie Su
paper_authors._id: 656d35129496f21be8a06ec1
paper_authors.name: Jilin Chen
paper_authors._id: 656d35129496f21be8a06ec2
paper_authors.name: Chinmay Kulkarni
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T10:17:24.764Z
paper_authors._id: 64fee57c2fa894e433e7d080
paper_authors.avatarUrl: /avatars/dd5140e40da4082a60f81a3d7d3db308.svg
paper_authors.fullname: Chinmay Kulkarni
paper_authors.type: user
paper_authors.user: kchinmay
paper_authors._id: 656d35129496f21be8a06ec3
paper_authors.name: Heng-Tze Cheng
paper_authors._id: 656d35129496f21be8a06ec4
paper_authors.name: Quoc Le
paper_authors._id: 656d35129496f21be8a06ec5
paper_authors.name: Ed Chi
paper_title: Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized
    Model Responses
paper_summary: 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.
paper_ai_keywords: Large language model (LLM) Chatbots Interactional cognitive load Exploratory tasks Sensemaking tasks Planning a trip Learning about a new city Textual interaction Bit scaffolding Informational "scent" High-level preferences Goal specification ExploreLLM Structured thoughts Thought exploration Option navigation Choice navigation Recommendation navigation Personalized responses User study Schema-like structure Task guidance Natural language Graphical user interfaces Integrated interaction