大语言模型对话合成与摘要能力的相互强化用于少样本对话摘要
Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization
AI总结：
提出MRDS机制，通过相互强化LLM的对话合成与摘要能力，以合成数据增强少样本训练，显著提升对话摘要性能。
摘要：
在这项工作中，我们提出了在大语言模型（LLM）内部的相互强化数据合成（MRDS）机制，以改进少样本对话摘要任务。与先前需要外部知识的方法不同，我们相互强化LLM的对话合成与摘要能力，使它们在训练过程中互补并提升整体性能。对话合成能力通过基于摘要能力的偏好评分进行定向偏好优化而增强。摘要能力则通过对话合成能力产生的高质量对话-摘要配对数据得到增强。通过利用所提出的MRDS机制，我们以合成数据的形式激发LLM的内部知识，并用其扩充少样本真实训练数据集。实验结果表明，我们的方法改进了对话摘要，在少样本设置下ROUGE分数提升了1.5%，BERT分数提升了0.3%。此外，我们的方法在人工评估中取得了最高的平均分数，超越了预训练模型和仅针对摘要任务微调的基线模型。
英文摘要：
In this work, we propose Mutual Reinforcing Data Synthesis (MRDS) within LLMs to improve few-shot dialogue summarization task. Unlike prior methods that require external knowledge, we mutually reinforce the LLMś dialogue synthesis and summarization capabilities, allowing them to complement each other during training and enhance overall performances. The dialogue synthesis capability is enhanced by directed preference optimization with preference scoring from summarization capability. The summarization capability is enhanced by the additional high quality dialogue-summary paired data produced by the dialogue synthesis capability. By leveraging the proposed MRDS mechanism, we elicit the internal knowledge of LLM in the format of synthetic data, and use it to augment the few-shot real training dataset. Empirical results demonstrate that our method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings. Furthermore, our method attains the highest average scores in human evaluations, surpassing both the pre-trained models and the baselines fine-tuned solely for summarization tasks.
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。
