LLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning
#LLM #multiturn dialogue #task-oriented #reasoning dataset #benchmarking #synthetic data #optimization #domain constraints #arXiv
📌 Key Takeaways
- LLM reasoning—analyzing, inferring, deciding—is crucial for intelligent task‑oriented dialogue systems.
- Current benchmarks are overly abstract and disconnected from realistic task flows and domain constraints.
- Data contamination from pre‑training corpora undermines reliable evaluation.
- Traditional crowdsourcing is labor‑intensive and hard to scale for dataset construction.
- Proposal: an LLM‑driven, trilevel optimization framework that synthesizes multi‑turn dialogues grounded in authentic task scenarios.
- The synthetic dataset includes enriched real‑world information and ensures strong contextual coherence.
- Corresponding reasoning tasks are iteratively refined to elevate difficulty and quality.
- Experimental results indicate the new dataset introduces non‑trivial reasoning challenges and supports LLM reasoning improvement.
📖 Full Retelling
Who: Researchers Yu Zhu and Kai Yang. What: A large‑language‑model (LLM) driven framework for synthesizing realistic, multi‑turn, task‑oriented dialogues to improve logical reasoning benchmarks. Where: The work was presented as an arXiv preprint in the Computation and Language (cs.CL) and Artificial Intelligence (cs.AI) categories. When: It was submitted on 27 February 2026. Why: Existing reasoning datasets are too simplistic, lack real‑world constraints, and suffer from pre‑training data contamination, limiting their usefulness for evaluating and improving LLM reasoning abilities.
🏷️ Themes
Large Language Models, Task‑Oriented Dialogue Systems, Logical Reasoning, Data Benchmarking, Synthetic Data Generation, Trilevel Optimization, Real‑World Scenario Grounding
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Original Source
--> Computer Science > Computation and Language arXiv:2602.23610 [Submitted on 27 Feb 2026] Title: LLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning Authors: Yu Zhu , Kai Yang View a PDF of the paper titled LLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning, by Yu Zhu and 1 other authors View PDF HTML Abstract: The reasoning capability of large language models , defined as their ability to analyze, infer, and make decisions based on input information, is essential for building intelligent task-oriented dialogue systems. However, existing benchmarks do not sufficiently reflect the complexity of real-world scenarios, which limits their effectiveness in evaluating and enhancing LLM reasoning in practical contexts. Many current reasoning datasets are overly simplistic and abstract, often disconnected from realistic task flows, domain constraints, and operational rules, making it difficult to effectively evaluate LLMs' logical reasoning ability. In addition, data contamination from pretraining corpora undermines the reliability of evaluation results, and traditional crowdsourcing methods for dataset construction are labor-intensive and difficult to scale. To address these challenges, we propose a LLM-driven framework for synthesizing multi-turn, task-oriented dialogues grounded in realistic reasoning scenarios, leveraging trilevel optimization to enhance dialogue quality. Our method generates dialogues grounded in authentic task scenarios, enriched with real-world information, and exhibiting strong contextual coherence. Corresponding reasoning tasks are carefully designed around these dialogues and iteratively refined to continuously improve the tasks' quality and challenge. The resulting dataset serves as a valuable benchmark for assessing and advancing the realistic logical reasoning capabilities of LLMs. Experimental results show that our synthetic data-based reasoning tasks introduce non-trivial reasoning chall...
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