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iToT: An Interactive System for Customized Tree-of-Thought Generation

Alan Boyle, Isha Gupta, Sebastian Hönig, Lukas Mautner, Kenza Amara, Furui Cheng, Mennatallah El-Assady

TL;DR

The paper addresses the barrier to applying Tree-of-Thought (ToT) prompting due to setup overhead and lack of interactivity. It proposes iToT, an interactive, web-based ToT system with a visual tree, semantic grouping of thoughts, and mixed-initiative user input. Key contributions include an interactive dashboard, a semantic grouping mechanism to reduce redundancy, onboarding features, and three human-LLM co-writing case studies. Results demonstrate that iToT enables users to observe and steer the model's reasoning, improving adaptability and transparency in complex tasks.

Abstract

As language models have become increasingly successful at a wide array of tasks, different prompt engineering methods have been developed alongside them in order to adapt these models to new tasks. One of them is Tree-of-Thoughts (ToT), a prompting strategy and framework for language model inference and problem-solving. It allows the model to explore multiple solution paths and select the best course of action, producing a tree-like structure of intermediate steps (i.e., thoughts). This method was shown to be effective for several problem types. However, the official implementation has a high barrier to usage as it requires setup overhead and incorporates task-specific problem templates which are difficult to generalize to new problem types. It also does not allow user interaction to improve or suggest new thoughts. We introduce iToT (interactive Tree-of-Thoughts), a generalized and interactive Tree of Thought prompting system. iToT allows users to explore each step of the model's problem-solving process as well as to correct and extend the model's thoughts. iToT revolves around a visual interface that facilitates simple and generic ToT usage and transparentizes the problem-solving process to users. This facilitates a better understanding of which thoughts and considerations lead to the model's final decision. Through three case studies, we demonstrate the usefulness of iToT in different human-LLM co-writing tasks.

iToT: An Interactive System for Customized Tree-of-Thought Generation

TL;DR

The paper addresses the barrier to applying Tree-of-Thought (ToT) prompting due to setup overhead and lack of interactivity. It proposes iToT, an interactive, web-based ToT system with a visual tree, semantic grouping of thoughts, and mixed-initiative user input. Key contributions include an interactive dashboard, a semantic grouping mechanism to reduce redundancy, onboarding features, and three human-LLM co-writing case studies. Results demonstrate that iToT enables users to observe and steer the model's reasoning, improving adaptability and transparency in complex tasks.

Abstract

As language models have become increasingly successful at a wide array of tasks, different prompt engineering methods have been developed alongside them in order to adapt these models to new tasks. One of them is Tree-of-Thoughts (ToT), a prompting strategy and framework for language model inference and problem-solving. It allows the model to explore multiple solution paths and select the best course of action, producing a tree-like structure of intermediate steps (i.e., thoughts). This method was shown to be effective for several problem types. However, the official implementation has a high barrier to usage as it requires setup overhead and incorporates task-specific problem templates which are difficult to generalize to new problem types. It also does not allow user interaction to improve or suggest new thoughts. We introduce iToT (interactive Tree-of-Thoughts), a generalized and interactive Tree of Thought prompting system. iToT allows users to explore each step of the model's problem-solving process as well as to correct and extend the model's thoughts. iToT revolves around a visual interface that facilitates simple and generic ToT usage and transparentizes the problem-solving process to users. This facilitates a better understanding of which thoughts and considerations lead to the model's final decision. Through three case studies, we demonstrate the usefulness of iToT in different human-LLM co-writing tasks.
Paper Structure (11 sections, 7 figures, 1 table)

This paper contains 11 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. iToT System Design
  4. Users and Tasks
  5. ToT Interaction Overview
  6. iToT Initialization
  7. Thought Generation
  8. Thought Evaluation
  9. User Onboarding
  10. Case Study
  11. Conclusion and Discussion

Figures (7)

  • Figure 1: The landing page features onboarding guidance (1) and some example tasks for a quick start (2). The sidebar leads to the user history and tree settings (3). There is an input field for the main prompt (task description) (4) as well as the two system prompts (5).
  • Figure 2: iToT enables users to configure the ToT process through the Setting Panel.
  • Figure 3: The user is enabled to interact with the model's thought process by adding new thoughts (1) and dynamically changing the settings for each generated layer (2). The user's "active path", i.e. the one being currently explored, is highlighted yellow (3) and they can expand and collapse subtrees for readability (4). The model supplies insights and explainability back to the user: Similar thoughts are grouped (a) and thought generation is accompanied by real-time status updates (b). The user can view the model's ranking of the generated thoughts (c) and the model's preferred path is highlighted green.
  • Figure 4: The first model response for the "Mathematical Proof" case study. The option evaluated as the best by the model (highlighted in green) is wrong and the option chosen by the user (highlighted in yellow) is correct.
  • Figure 5: The user is not fully satisfied with the model's conclusion and adds a thought expressing the step the user believes to be missing. In response, the model provides a more elaborate conclusion to the proof.
  • ...and 2 more figures