LLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning

Researchers propose an LLM-driven framework for generating multi-turn task-oriented dialogues to create more realistic reasoning benchmarks. The framework addresses limitations in current AI evaluation methods by producing synthetic datasets that better reflect real-world complexity and contextual coherence.

• →Current LLM reasoning benchmarks are too simplistic and disconnected from real-world scenarios.
• →A new trilevel optimization framework generates more realistic task-oriented dialogue datasets.
• →The synthetic datasets provide better evaluation of LLMs' logical reasoning capabilities.
• →Traditional crowdsourcing methods for dataset construction are labor-intensive and don't scale well.
• →Experimental results show the synthetic data introduces meaningful reasoning challenges for LLM improvement.