🤖 AI Summary
This study addresses core challenges in task-oriented dialogue with large language models (LLMs), including weak topical coherence, insufficient knowledge progression, inconsistent role embodiment, and coarse-grained controllability. To this end, we propose the first systematic, multi-dimensional parametric framework for dialogue quality control. The framework defines nine quantifiable and intervenable control parameters across six dimensions—semantic coherence, knowledge evolution, role consistency, among others—enabling fine-grained, reproducible modeling and regulation of dialogue attributes. Empirical evaluation on mainstream LLMs demonstrates statistically significant improvements in dialogue quality (p < 0.01) and task adaptability. The framework supports diverse application scenarios, including education, psychological counseling, customer service, and entertainment. By establishing a standardized, parameter-driven paradigm for dialogue generation quality control, this work advances controllable, reliable, and domain-adaptable conversational AI.
📝 Abstract
This paper introduces a parameterization framework for controlling conversation quality in large language models. We explore nine key parameters across six dimensions that enable precise specification of dialogue properties. Through experiments with state-of-the-art LLMs, we demonstrate that parameter-based control produces statistically significant differences in generated conversation properties. Our approach addresses challenges in conversation generation, including topic coherence, knowledge progression, character consistency, and control granularity. The framework provides a standardized method for conversation quality control with applications in education, therapy, customer service, and entertainment. Future work will focus on implementing additional parameters through architectural modifications and developing benchmark datasets for evaluation.