Summary of Interactive Evolution: a Neural-symbolic Self-training Framework For Large Language Models, by Fangzhi Xu et al.
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
by Fangzhi Xu, Qiushi Sun, Kanzhi Cheng, Jun Liu, Yu Qiao, Zhiyong Wu
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ENVISIONS framework is an environment-guided neural-symbolic self-training method designed to mitigate the reliance on human annotations in Large Language Models (LLMs). The framework aims to overcome two challenges: the scarcity of symbolic data and the limited proficiency of LLMs in processing symbolic language. The effectiveness of ENVISIONS is demonstrated through extensive evaluations conducted on three distinct domains, showcasing its potential for applications in neural-symbolic scenarios. Code will be available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super powerful because they use lots of human-annotated natural language data to learn. But that data isn’t always available, so scientists wanted to find a way to make LLMs work without it. They created a new method called ENVISIONS that helps LLMs understand symbolic language and work better with limited data. This is important because we need LLMs to be good at both natural and symbolic languages. The scientists tested ENVISIONS on three different kinds of problems and found that it worked really well. They also figured out what makes it successful, which will help them improve it in the future. |
Keywords
» Artificial intelligence » Self training
