hello! would appreciate being added, thank you!

Hello! Would love to be added to this, thank you so much!

TreeInstruct is preferred 78.43% of the time. It solves 14.09% more bugs across all settings, and our questions are 14.18% better at addressing bugs, maintaining relevance, and ensuring logical conversation flow. TreeInstruct also adapts to human students of varying backgrounds.

TreeInstruct estimates the knowledge a student needs to debug their code and devises a conversation plan.

It then dynamically constructs a question tree based on its interactions with the student, navigating the knowledge state space till the student comprehends & fixes all misunderstandings &bugs.

github.com/agarwalishik...
We apply TreeInstruct to code debugging. Prior works directly give away bugs/fixes, assume single-turn conversations, and only work for one bug. We create a realistic, multi-bug dataset, where the bugs can be either mutually independent or dependent.

Can LLMs make us critical thinkers?

TreeInstruct reorients assistant-like LLMs to be instructors that guide students towards understanding their mistakes, without providing direct/indirect answers.

Check out aclanthology.org/2024.finding... (w/ @wonderingishika.bsky.social) to learn more!