Summary of Relational Programming with Foundation Models, by Ziyang Li et al.
Relational Programming with Foundation Models
by Ziyang Li, Jiani Huang, Jason Liu, Felix Zhu, Eric Zhao, William Dodds, Neelay Velingker, Rajeev Alur, Mayur Naik
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Programming Languages (cs.PL)
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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 The proposed Vieira framework unifies mechanisms for augmenting powerful yet incomplete foundation models with capabilities such as in-context learning, information retrieval, and code interpreting. This declarative framework treats foundation models as stateless functions with relational inputs and outputs, enabling seamless combinations with logic programs or diverse sub-models. Vieira’s probabilistic relational paradigm supports neuro-symbolic applications and complex, multi-modal tasks. The framework is implemented by extending the Scallop compiler with a foreign interface supporting foundation models as plugins. Plugins for 12 foundation models, including GPT, CLIP, and SAM, are demonstrated. Evaluation on 9 challenging tasks shows that Vieira programs are concise, accurate, and comparable to competitive baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models have exciting potential for diverse AI applications. Researchers proposed a new framework called Vieira that helps these powerful but incomplete models do more things. It’s like a toolbox that lets you combine different AI models in new ways. Vieira uses a special way of thinking about data and relationships to make it easy to work with many types of AI models. The authors tested Vieira on 9 challenging tasks, including language, vision, and database problems. Their results show that Vieira makes it possible to write programs that are short, accurate, and effective. |
Keywords
» Artificial intelligence » Gpt » Multi modal » Sam
