LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues
This addresses the data bottleneck for improving task-oriented dialogue systems, enabling rapid bootstrapping for new domains, though it is incremental in automating data generation.
The paper tackles the scarcity of high-quality, challenging dialogue data for virtual assistants by introducing LUCID, an LLM-driven system that generated a dataset of 4,277 conversations across 100 intents, with human review confirming high-quality labels.
Spurred by recent advances in Large Language Models (LLMs), virtual assistants are poised to take a leap forward in terms of their dialogue capabilities. Yet a major bottleneck to achieving genuinely transformative task-oriented dialogue capabilities remains the scarcity of high quality data. Existing datasets, while impressive in scale, have limited domain coverage and contain few genuinely challenging conversational phenomena; those which are present are typically unlabelled, making it difficult to assess the strengths and weaknesses of models without time-consuming and costly human evaluation. Moreover, creating high quality dialogue data has until now required considerable human input, limiting both the scale of these datasets and the ability to rapidly bootstrap data for a new target domain. We aim to overcome these issues with LUCID, a modularised and highly automated LLM-driven data generation system that produces realistic, diverse and challenging dialogues. We use LUCID to generate a seed dataset of 4,277 conversations across 100 intents to demonstrate its capabilities, with a human review finding consistently high quality labels in the generated data.