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

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

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 this https URL

Artificial Intelligence (cs.AI)

arXiv:2609.04030 [cs.AI]

(or arXiv:2609.04030v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.04030 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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