Summary of Beyond Turn-based Interfaces: Synchronous Llms As Full-duplex Dialogue Agents, by Bandhav Veluri et al.
Beyond Turn-Based Interfaces: Synchronous LLMs as Full-Duplex Dialogue Agents
by Bandhav Veluri, Benjamin N Peloquin, Bokai Yu, Hongyu Gong, Shyamnath Gollakota
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 This paper proposes a novel approach to modeling spoken dialogue agents, allowing for full-duplex interaction that mimics human conversation. Traditional models are limited to half-duplex interactions, requiring explicit prompting or tracking of silence events. The challenge lies in integrating time information into large language models (LLMs) to enable synchrony with the real-world clock. The authors design a novel mechanism and training recipe using synthetic spoken dialogue data to create a model that generates meaningful and natural spoken dialogue. The Synchronous LLM outperforms state-of-the-art models in terms of dialogue meaningfulness while maintaining naturalness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us talk like humans on the phone or computer! Currently, computers can only respond when we’re finished talking, but humans have conversations all at once. The problem is that big language models don’t understand time. To fix this, researchers created a new way to make these models work with real-time clocks. They also used lots of pretend conversation data and some actual human conversations to train the model. This new model can have conversations just like us! It’s better than other computer conversations at being helpful while still sounding natural. |
Keywords
» Artificial intelligence » Prompting » Tracking
