用9个参数精准控制大模型对话质量，提升一致性与连贯性。
Can LLMs Generate High-Quality Task-Specific Conversations?
- 通过6个维度的9个参数调节对话属性
- 实验证明参数控制可显著改变对话质量
- 适合教育、心理治疗等需高质量对话场景
本文提出一种用于控制大语言模型对话质量的参数化框架。研究探索了六个维度下的九个关键参数，实现对对话特性的精确设定。在先进大模型上的实验表明，基于参数的控制能产生具有统计显著差异的对话特性。该方法解决了话题连贯性、知识推进、角色一致性及控制粒度等挑战。框架为对话质量控制提供了标准化方法，适用于教育、心理治疗、客户服务和娱乐等领域。未来工作将通过架构修改引入更多参数，并构建评估用基准数据集。
原文摘要 · Abstract (English)
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.
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