Summary of Data-efficient Learning with Neural Programs, by Alaia Solko-breslin et al.
Data-Efficient Learning with Neural Programs
by Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach for learning neural programs is introduced, which combines deep neural networks (DNNs) with traditional programming languages or API calls to large language models (LLMs). Neural programs are composites of DNNs followed by programmatic components, and the proposed algorithm, ISED, estimates gradients of black-box components using only input-output samples. The method is evaluated on new benchmarks that involve LLMs like GPT-4, as well as existing neurosymbolic learning benchmarks. Results show that ISED achieves comparable performance to state-of-the-art frameworks for neurosymbolic learning tasks, and outperforms baselines in terms of data efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer how to do a task that involves both thinking like humans and following rules like computers. This paper shows one way to make this work by combining two types of “thinking”: deep neural networks (like what brains use) and traditional programming languages or special AI helpers called large language models. They came up with a new method for teaching computers how to do these tasks, which they call ISED. It’s like a shortcut that lets the computer learn faster and more efficiently than before. |
Keywords
» Artificial intelligence » Gpt
