LUCID: Generating High-Quality Dialogue Data with Large Language Models

Core Concepts

Large Language Models (LLMs) can be used to generate high-quality dialogue data efficiently.

Abstract

LUCID is a data generation system that leverages Large Language Models (LLMs) to create diverse and challenging dialogues. It addresses the scarcity of high-quality dialogue data for virtual assistants by automating the process. The system generates multi-domain, multi-intent conversations with challenging conversational phenomena. LUCID's validation process ensures accurate system labels, leading to reliable evaluation results. The dataset includes 100 intents across 13 domains, showcasing the system's ability to create complex data efficiently.
LUCID decomposes the data generation process into multiple stages involving intent generation, conversation planning, turn-by-turn conversation generation, and LLM-based validation. The system ensures diversity in slot values and intents, surpassing existing task-oriented dialogue datasets in terms of complexity and labeled conversational phenomena. Additionally, LUCID provides detailed analysis and baseline results for evaluating the generated data.
The system's extensibility allows for large-scale data generation with more intents, domains, and complex conversational phenomena. By releasing both the code and dataset, LUCID aims to facilitate further research in generating high-quality dialogue data using LLMs.

Stats

Existing datasets include multi-turn, multi-intent conversations.
LUCID dataset contains 100 intents across 13 domains.
Seed dataset consists of 4,277 dialogues with 92,699 turns.

Quotes

"Virtual assistants are poised to take a dramatic leap forward in terms of their dialogue capabilities."
"We aim to overcome issues with high quality dialogue data using LUCID."
"LUCID generates diverse and challenging dialogues efficiently."

Key Insights Distilled From

LUCID

by Joe Stacey,J... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00462.pdf

Deeper Inquiries

How can LUCID's automated methodology improve the scalability of generating high-quality dialogue data?

LUCID's automated methodology enhances scalability by breaking down the data generation process into manageable steps that Large Language Models (LLMs) can perform accurately. By using a pipeline of modularized LLM calls, LUCID compartmentalizes the task into simpler components, allowing for consistent and efficient generation of realistic dialogues across various intents and domains. This automation reduces the need for extensive human involvement in crafting dialogue data, enabling rapid scaling to new target domains without compromising on quality.

What are the potential limitations of relying solely on Large Language Models for data generation?

While Large Language Models (LLMs) offer significant advantages in generating dialogue data, there are some limitations to consider. One key limitation is the potential for bias or inaccuracies in generated content due to biases present in pretraining data or model architecture. Additionally, LLMs may struggle with understanding nuanced context or complex conversational phenomena accurately, leading to errors or unrealistic responses in generated dialogues. Moreover, relying solely on LLMs may result in a lack of diversity and creativity in generated content as models tend to prioritize common patterns from training data.

How might incorporating human input enhance the quality of dialogue datasets generated by systems like LUCID?

Incorporating human input can significantly enhance the quality of dialogue datasets generated by systems like LUCID by providing valuable insights and oversight that machines alone may not capture effectively. Human input can help validate and correct inaccuracies or biases present in machine-generated dialogues, ensuring higher accuracy and relevance. Humans can also contribute domain expertise, linguistic nuances, and real-world knowledge that enriches the dataset with diverse perspectives and authentic conversational elements. Additionally, human annotators can identify subtle errors or inconsistencies that automated systems might overlook, leading to more robust and reliable dialogue datasets overall.