Although pre-trained language models have remarkably enhanced the generation
ability of dialogue systems, open-domain Chinese dialogue systems are still
limited by the dialogue data and the model size compared with English ones. In
this paper, we propose EVA, a Chinese dialogue system that contains the largest
Chinese pre-trained dialogue model with 2.8B parameters. To build this model,
we collect the largest Chinese dialogue dataset named WDC-Dialogue from various
public social media. This dataset contains 1.4B context-response pairs and is
used as the pre-training corpus of EVA. Extensive experiments on automatic and
human evaluation show that EVA outperforms other Chinese pre-trained dialogue
models especially in the multi-turn interaction of human-bot conversations.

本文提出了一个包含最大的 2.8B 参数的中文对话系统 EVA，使用最大的中文对话数据集 WDC-Dialogue 进行预训练，实验证明 EVA 在人机会话的多轮交互中表现优异，超过了其他中文预训练对话模型。

EVA：一个基于大规模生成预训练的开放域中文对话系统

EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative  Pre-Training

Language generation models' democratization benefits many domains, from
answering health-related questions to enhancing education by providing
AI-driven tutoring services. However, language generation models'
democratization also makes it easier to generate human-like text at-scale for
nefarious activities, from spreading misinformation to targeting specific
groups with hate speech. Thus, it is essential to understand how people
interact with bots and develop methods to detect bot-generated text. This paper
shows that bot-generated text detection methods are more robust across datasets
and models if we use information about how people respond to it rather than
using the bot's text directly. We also analyze linguistic alignment, providing
insight into differences between human-human and human-bot conversations.

本文研究了语言生成模型的民主化对人们与机器人互动方式以及识别机器人生成的文本方法的影响，并发现结合人类对话反馈信息的机器人生成文本识别方法更加稳健，同时对人机会话的语言统计学变化进行了分析。