Summary of How to Correctly Do Semantic Backpropagation on Language-based Agentic Systems, by Wenyi Wang et al.
How to Correctly do Semantic Backpropagation on Language-based Agentic Systems
by Wenyi Wang, Hisham A. Alyahya, Dylan R. Ashley, Oleg Serikov, Dmitrii Khizbullin, Francesco Faccio, Jürgen Schmidhuber
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Machine Learning (stat.ML)
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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 Language-based agentic systems have been successfully deployed in real-world tasks, but optimizing these systems often requires significant manual labor. Recent studies have shown that these systems can be represented as computational graphs, enabling automatic optimization. However, most current efforts in Graph-based Agentic System Optimization (GASO) fail to properly assign feedback to the system’s components given feedback on the output. To address this challenge, we formalize the concept of semantic backpropagation with semantic gradients, a generalization that aligns several key optimization techniques. This method computes directional information about how changes to each component might improve the system’s output. We propose a method called semantic gradient descent, which enables us to solve GASO problems effectively. Our results on BIG-Bench Hard and GSM8K show that our approach outperforms existing state-of-the-art methods for solving GASO problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how we optimize language-based systems. These systems can be very good at doing certain tasks, but making them better requires a lot of human effort. The authors found a way to represent these systems as graphs, which makes it easier to make changes and improve their performance. One problem they solved was figuring out how to give feedback to different parts of the system based on its output. They came up with a new method called semantic gradient descent that helps them optimize the system better. The results show that this new method works well for solving certain types of problems. |
Keywords
» Artificial intelligence » Backpropagation » Generalization » Gradient descent » Optimization
