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

arXiv:2609.04030 · cs.AI · Submitted 2026-09-03 · Read on arXiv

cs.AI

Submitted: 2026-09-03

Updated: 2026-09-03

Code: https://github.com/lcroy/ToD4IR

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 13× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Perplexity — 4× in this paper · explained in 3 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Annotation Errors — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• Schema-Guided Dialogue — 2× in this paper
• AI Agents — 1× in this paper · explained in 2 episodes
• Airline Travel Information System — 1× in this paper
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• LLM Integration — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Slot Accuracy — 1× in this paper
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning
• When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs

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