Summary of Dard: a Multi-agent Approach For Task-oriented Dialog Systems, by Aman Gupta et al.
DARD: A Multi-Agent Approach for Task-Oriented Dialog Systems
by Aman Gupta, Anirudh Ravichandran, Ziji Zhang, Swair Shah, Anurag Beniwal, Narayanan Sadagopan
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 DARD (Domain Assigned Response Delegation) is a multi-agent conversational system that successfully handles multi-domain dialogs by leveraging domain-specific agents orchestrated by a central dialog manager agent. The system combines the strengths of smaller fine-tuned models, such as Flan-T5-large and Mistral-7B, with those of larger Large Language Models (LLMs), like Claude Sonnet 3.0. This approach allows for flexibility and composability, leading to state-of-the-art performance on the MultiWOZ benchmark. The system achieves a dialogue inform rate improvement of 6.6% and a success rate improvement of 4.1% over existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DARD is a special kind of computer program that helps humans have conversations with each other. It’s really good at handling lots of different topics and styles, like talking about the weather or making reservations. The program uses smaller computers (called “agents”) to help it understand what people are saying and respond in a way that makes sense. This lets DARD talk to people more naturally and make better decisions. In fact, it’s so good that it beat other programs on a special test called MultiWOZ. |
Keywords
» Artificial intelligence » Claude » T5
