Summary of Bel Esprit: Multi-agent Framework For Building Ai Model Pipelines, by Yunsu Kim et al.
Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines
by Yunsu Kim, AhmedElmogtaba Abdelaziz, Thiago Castro Ferreira, Mohamed Al-Badrashiny, Hassan Sawaf
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); 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 The paper introduces Bel Esprit, a conversational agent that constructs AI model pipelines based on user-defined requirements. The multi-agent framework clarifies requirements, builds, validates, and populates pipelines with appropriate models. It demonstrates effectiveness in generating pipelines from ambiguous user queries using human-curated and synthetic data. A detailed error analysis highlights ongoing challenges in pipeline construction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bel Esprit is a new way to make AI model pipelines that can understand what people want. Right now, it’s hard to build the right combination of models to solve complex problems. Bel Esprit makes this easier by using multiple “sub-agents” that work together to figure out what someone wants and then build a pipeline with the right models. This helps even when the user isn’t very clear about what they want. The paper shows how well it works using real data and fake data, and talks about some of the challenges that still need to be solved. |
Keywords
» Artificial intelligence » Synthetic data
