metadata
license: apache-2.0
Interactive Evolution: A Neural-Symbolic Self-Training Framework for Large Language Models
Paper Link: https://arxiv.org/abs/2406.11736
Code Repo: https://github.com/xufangzhi/ENVISIONS
🔥 News
- 🔥🔥🔥 We make public the final checkpoints after self-training ! ! !
Note
The self-training process is based on LLaMA2-Chat model serieses and powered by ENVISIONS. The work is still under review.
Prompt for Zero-shot Evaluation
You are required to navigate the web. To accomplish the task, use methods in Agent class to generate actions, with the following functions.
type(characters: str): Type a string via the keyboard.
click_xpath(xpath: str): Click an HTML element with a valid XPath.
press(key_type: str): Press a key on the keyboard (enter, space, arrowleft, arrowright, backspace, arrowup, arrowdown, command+a, command+c, command+v).
click_option(xpath: str): Click an option HTML element in a list with a valid XPath.
movemouse(xpath: str): Move the mouse cursor on an HTML element with a valid XPath.
The observation is: <observation>
The action is:
Citation
If you find it helpful, please kindly cite the paper.
@misc{xu2024interactive,
title={Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models},
author={Fangzhi Xu and Qiushi Sun and Kanzhi Cheng and Jun Liu and Yu Qiao and Zhiyong Wu},
year={2024},
eprint={2406.11736},
archivePrefix={arXiv},
}