Summary of Language Agents As Optimizable Graphs, by Mingchen Zhuge et al.
Language Agents as Optimizable Graphs
by Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper unifies various prompt engineering techniques for Large Language Models (LLMs) by representing LLM-based agents as computational graphs. The authors introduce a novel framework that optimizes both node-level prompts and agent orchestration, enabling the efficient development, integration, and automatic improvement of multiple LLM agents. Experimental results demonstrate the effectiveness of this approach in improving problem solvers based on LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make computers smarter by using special language models called Large Language Models (LLMs). The problem is that there are many different ways to make these computers better, and it’s hard to keep track of them all. So, the authors came up with a way to organize all these methods into something like a flowchart. This lets us combine them in new ways to make even smarter computers. They tested their idea and showed that it really works. |
Keywords
* Artificial intelligence * Prompt
