Summary of From Summary to Action: Enhancing Large Language Models For Complex Tasks with Open World Apis, by Yulong Liu et al.
From Summary to Action: Enhancing Large Language Models for Complex Tasks with Open World APIs
by Yulong Liu, Yunlong Yuan, Chunwei Wang, Jianhua Han, Yongqiang Ma, Li Zhang, Nanning Zheng, Hang Xu
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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 paper proposes a novel approach to augment Large Language Models (LLMs) with the ability to learn external tool usage, which could be a crucial step towards achieving artificial general intelligence. The authors introduce a pipeline called “from Summary to action” (Sum2Act), designed to control massive real-world APIs and mirror the human task-solving process. This pipeline involves guiding LLMs to summarize achieved results and determine the next course of action. Experimental evaluations on the ToolBench benchmark show significant performance improvements compared to established methods like ReAct and DFSDT, highlighting Sum2Act’s effectiveness in enhancing LLMs for complex real-world tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping computers learn from humans by using tools just like we do. It’s like teaching a computer how to use a hammer or a screwdriver. The authors created a new way to make large language models (like chatbots) better at learning and doing things on their own. They call it “from Summary to action” (Sum2Act). This process helps the model understand what it has done so far and decide what to do next. The results show that this method is better than previous ones, which could be important for making computers more intelligent. |
