DiaSynth：面向低资源对话应用的合成对话生成框架
DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications
AI总结：
针对特定领域对话数据集稀缺限制对话系统发展的问题，提出基于大型语言模型（LLMs）和思维链（CoT）推理的DiaSynth合成对话生成框架，实验表明其生成的合成数据可提升对话摘要模型性能，且质量随LLM规模增大而提升，为低资源对话应用提供数据支持。
摘要：
特定领域对话数据集的稀缺限制了对话系统在各类应用中的发展。现有研究受限于通用或小众数据集，这些数据集缺乏足够规模用于训练对话系统。为填补这一空白，我们引入DiaSynth——一个合成对话生成框架，能够在广泛领域生成高质量、上下文丰富的对话。与现有框架不同，DiaSynth使用大型语言模型（LLMs）和思维链（CoT）推理，生成具有模拟角色和多样会话特征的动态、特定领域对话。我们通过使用不同LLM和来自DialogSum与SAMSum的少量示例生成合成数据来开展实验。在合成数据上微调的预训练语言模型在对话摘要任务上比基础模型性能提升16.47%，而在领域内数据与合成数据上微调的模型对比显示，合成数据能捕捉领域内数据在对话摘要任务上90.48%的性能分布。随着LLM规模从3B增至8B，生成数据的质量也随之提升。这些结果验证了DiaSynth作为传统数据收集方法可靠替代方案的潜力。我们开源了代码和生成的数据以供未来研究使用。
英文摘要：
The scarcity of domain-specific dialogue datasets limits the development of dialogue systems across applications. Existing research is constrained by general or niche datasets that lack sufficient scale for training dialogue systems. To address this gap, we introduce DiaSynth - a synthetic dialogue generation framework capable of generating high-quality, contextually rich dialogues across a wide range of domains. Unlike existing frameworks, DiaSynth uses Large Language Models (LLMs) and Chain of Thought (CoT) reasoning to generate dynamic, domain-specific dialogues with simulated personas and diverse conversational features. We perform our experiments by generating synthetic data using different LLMs and few-shot examples from DialogSum and SAMSum. The pretrained language models fine-tuned on the synthetic data outperform the base models by 16.47% on dialogue summarization, while the comparison between models fine-tuned on in-domain data and synthetic data shows that the synthetic data is able to capture 90.48% of the performance distribution of the in-domain data on dialogue summarization. The quality of the data generated also increases as we increase the size of LLM from 3B to 8B. These results validate DiaSynth's potential as a robust alternative to traditional data collection methods. We open source the code and data generated for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
