Summary of Marco: Multi-agent Real-time Chat Orchestration, by Anubhav Shrimal et al.
MARCO: Multi-Agent Real-time Chat Orchestration
by Anubhav Shrimal, Stanley Kanagaraj, Kriti Biswas, Swarnalatha Raghuraman, Anish Nediyanchath, Yi Zhang, Promod Yenigalla
First submitted to arxiv on: 29 Oct 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 MARCO is a Multi-Agent Real-time Chat Orchestration framework that leverages large language models (LLMs) to automate complex, real-world problems. The framework addresses key challenges in LLM utilization by incorporating robust guardrails to steer behavior, validate outputs, and recover from errors. MARCO demonstrates superior performance with 94.48% accuracy on the Digital Restaurant Service Platform conversations dataset and 92.74% accuracy on the Retail conversations dataset, along with 44.91% improved latency and 33.71% cost reduction. The framework’s modular design allows it to be adapted for automating tasks across domains and executing complex usecases through multi-turn interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super-smart AI chatbot that can help us do things like order food or shop online. But making these chatbots is hard because they need to understand lots of different tools, people, and rules. MARCO is a new way to make these chatbots better. It helps the AI learn how to talk correctly and fix mistakes when it gets confused. With MARCO, the chatbot can do tasks like ordering food or shopping online much faster and cheaper than before. This makes it useful for lots of different situations. |
