Academic

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

arXiv:2602.23610v1 Announce Type: new Abstract: The reasoning capability of large language models (LLMs), 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-tur

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Yu Zhu, Kai Yang · · 1 min read · 16 views
#cs.CL #cs.AI

arXiv:2602.23610v1 Announce Type: new Abstract: The reasoning capability of large language models (LLMs), 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 challenges and provide meaningful support for improving the reasoning capabilities of LLMs.

Executive Summary

This article proposes a novel framework for synthesizing multi-turn, task-oriented dialogues leveraging large language models (LLMs) to enhance dialogue quality and realism. The framework addresses existing challenges in evaluating LLMs' logical reasoning ability by generating dialogues grounded in authentic task scenarios, enriched with real-world information, and exhibiting strong contextual coherence. Experimental results demonstrate the effectiveness of the proposed framework in introducing non-trivial reasoning challenges and improving the reasoning capabilities of LLMs. The resulting dataset serves as a valuable benchmark for assessing and advancing the realistic logical reasoning capabilities of LLMs.

Key Points

  • ▸ The article addresses existing limitations in evaluating LLMs' logical reasoning ability.
  • ▸ The proposed framework leverages trilevel optimization to enhance dialogue quality and realism.
  • ▸ The resulting dataset serves as a valuable benchmark for assessing and advancing the realistic logical reasoning capabilities of LLMs.

Merits

Strength 1: Enhanced Realism

The proposed framework generates dialogues grounded in authentic task scenarios, enriched with real-world information, and exhibiting strong contextual coherence, which enhances the realism of the generated dialogues.

Strength 2: Improved Reasoning Challenges

The experimental results demonstrate the effectiveness of the proposed framework in introducing non-trivial reasoning challenges, which improves the reasoning capabilities of LLMs.

Demerits

Limitation 1: Data Contamination

The article acknowledges that data contamination from pretraining corpora may undermine the reliability of evaluation results, which is a limitation of the proposed framework.

Limitation 2: Labor-Intensive Dataset Construction

The article notes that traditional crowdsourcing methods for dataset construction are labor-intensive and difficult to scale, which is a limitation of the proposed framework.

Expert Commentary

The proposed framework is a significant contribution to the field of natural language processing, particularly in the area of dialogue systems. The use of trilevel optimization to enhance dialogue quality and realism is a novel and innovative approach. However, the article could have benefited from a more detailed discussion of the limitations of the proposed framework and potential avenues for future research. Additionally, the article could have provided more information on the scalability and generalizability of the proposed framework.

Recommendations

  • ✓ Future research should investigate the scalability and generalizability of the proposed framework.
  • ✓ Future research should explore the application of the proposed framework in different domains and scenarios.

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