用大规模自动生成的对话数据训练模型，提升对话角色一致性。
Dialogue Language Model with Large-Scale Persona Data Engineering
- 通过自动提取构建超大规模对话人格数据集
- 在多个评测中表现优于现有模型，人格一致性显著提升
- 适合需要稳定角色设定的对话系统开发者
在开放域对话系统中，保持角色一致性至关重要，如ChatGPT所示。尽管已有进展，当前人格对话数据集规模和多样性仍有限，制约了模型鲁棒性。受大规模预训练成功的启发，本文提出PPDS，一种基于大规模生成预训练的人格对话系统。我们设计了一种人格提取模型，可自主、精准生成海量人格对话数据；同时提出首创的人格增强技术，解决构建数据集中存在的无效人格偏差问题。定量与人工评估一致表明，所提模型在回复质量与人格一致性上均表现更优，验证了其有效性。
原文摘要 · Abstract (English)
Maintaining persona consistency is paramount in the application of open-domain dialogue systems, as exemplified by models like ChatGPT. Despite significant advancements, the limited scale and diversity of current persona dialogue datasets remain challenges to achieving robust persona-consistent dialogue models. In this study, drawing inspiration from the success of large-scale pre-training, we introduce PPDS, an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialogue dataset to enhance persona consistency. Specifically, we present a persona extraction model designed to autonomously and precisely generate vast persona dialogue datasets. Additionally, we unveil a pioneering persona augmentation technique to address the invalid persona bias inherent in the constructed dataset. Both quantitative and human evaluations consistently highlight the superior response quality and persona consistency of our proposed model, underscoring its effectiveness.
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