IRWOZ 2.0: A Large Language Model-driven Dialogue Dataset for Industrial Robot Conversations

2026-09-03

AI Summary

本文通过使用大型语言模型增强生成和质量改进解决了IRWOZ初版对话状态与话语中的噪声问题，提升了工业机器人对话系统的状态跟踪准确性。

Abstract

IRWOZ has improved industrial human-robot interaction (HRI) dialogue systems through domain-specific annotations. However, its initial version contains substantial noise in dialogue states and utterances, limiting state-tracking accuracy. We introduce IRWOZ 2.0, which addresses these limitations through large language model (LLM) enhanced generation (Mistral/Claude-3.5) and quality refinements. Our improved dataset expands to 390 dialogues across 4 industrial domains (Assembly, Delivery, Position, Relocation), featuring manual corrections and automated typo removal. Benchmark experiments on dialogue state tracking demonstrate significant improvements, with GPT-2's BLEU-4 score increasing from 0.1651 to 0.5604 compared to original IRWOZ. To support industrial HRI research, we publicly released IRWOZ 2.0 dataset at https://ieee-dataport.org/documents/irwoz-20-large-language-model-driven-dialogue-dataset-industrial-robot-conversations

Problem

• dialogue states
• utterances
• noise
• state-tracking accuracy

Innovation

• Large Language Model
• Dialogue State Tracking
• Industrial HRI
• Quality Refinements
• Benchmark Experiments