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

IRWOZ has improved industrial human-robot interaction (HRI) dialogue systems through domain-specific annotations. It comes from a paper posted to arXiv on 4 September 2026. 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

Why it counts

IRWOZ has improved industrial human-robot interaction (HRI) dialogue systems through domain-specific annotations. Our improved dataset expands to 390 dialogues across 4 industrial domains (Assembly, Delivery, Position, Relocation), featuring manual corrections and automated typo removal.

Sources

Primary source: primary source

What is not known

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