Large Language Models Enable Automated Formative Feedback in Human-Robot Interaction Tasks
Robotics
2024-05-28 v1
Abstract
We claim that LLMs can be paired with formal analysis methods to provide accessible, relevant feedback for HRI tasks. While logic specifications are useful for defining and assessing a task, these representations are not easily interpreted by non-experts. Luckily, LLMs are adept at generating easy-to-understand text that explains difficult concepts. By integrating task assessment outcomes and other contextual information into an LLM prompt, we can effectively synthesize a useful set of recommendations for the learner to improve their performance.
Cite
@article{arxiv.2405.16344,
title = {Large Language Models Enable Automated Formative Feedback in Human-Robot Interaction Tasks},
author = {Emily Jensen and Sriram Sankaranarayanan and Bradley Hayes},
journal= {arXiv preprint arXiv:2405.16344},
year = {2024}
}
Comments
Presented at Human-LLM Interaction Workshop at HRI 2024