iToT: An Interactive System for Customized Tree-of-Thought Generation
<|reference_start|>iToT: An Interactive System for Customized Tree-of-Thought Generation: 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.<|reference_end|>
@article{boyle2024itot:,
    title={iToT: An Interactive System for Customized Tree-of-Thought Generation},
    author={Alan Boyle, Isha Gupta, Sebastian H"onig, Lukas Mautner, Kenza Amara,
  Furui Cheng, and Mennatallah El-Assady},
    journal={arXiv preprint arXiv:2409.00413},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00413},
    primaryClass={cs.HC}
}