Summary of Large Language Models As User-agents For Evaluating Task-oriented-dialogue Systems, by Taaha Kazi et al.
Large Language Models as User-Agents for Evaluating Task-Oriented-Dialogue Systems
by Taaha Kazi, Ruiliang Lyu, Sizhe Zhou, Dilek Hakkani-Tur, Gokhan Tur
First submitted to arxiv on: 15 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 Our paper proposes a novel approach to evaluating task-oriented dialogue (TOD) systems by leveraging large language models (LLMs) as user-agents. Traditional offline datasets lack context awareness, making them suboptimal for conversational systems. We utilize LLMs to create user-agents that simulate human conversations, prompting them with in-context examples and tracking the user-goal state. Our evaluation shows improved performance on diversity and task completion metrics using better prompts. Additionally, we introduce methodologies for automatic TOD model evaluation within this dynamic framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re talking to a computer. Most of the time, people test these systems using old data that doesn’t understand what’s happening in real conversations. That’s not very helpful. Our team came up with a new way to test these systems by creating “virtual users” that behave more like humans. We used big language models to make these virtual users and taught them how to have conversations. By testing our approach, we found that using better prompts made the virtual users perform better. We also introduced ways to automatically evaluate these conversation systems. This is important because it helps us create more realistic and helpful computer conversations. |
Keywords
» Artificial intelligence » Prompting » Tracking
