We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters
capable of knowledge-grounded conversation in Chinese using a search engine to
access the Internet knowledge. GLM-Dialog offers a series of applicable
techniques for exploiting various external knowledge including both helpful and
noisy knowledge, enabling the creation of robust knowledge-grounded dialogue
LLMs with limited proper datasets. To evaluate the GLM-Dialog more fairly, we
also propose a novel evaluation method to allow humans to converse with
multiple deployed bots simultaneously and compare their performance implicitly
instead of explicitly rating using multidimensional metrics.Comprehensive
evaluations from automatic to human perspective demonstrate the advantages of
GLM-Dialog comparing with existing open source Chinese dialogue models. We
release both the model checkpoint and source code, and also deploy it as a
WeChat application to interact with users. We offer our evaluation platform
online in an effort to prompt the development of open source models and
reliable dialogue evaluation systems. The additional easy-to-use toolkit that
consists of short text entity linking, query generation, and helpful knowledge
classification is also released to enable diverse applications. All the source
code is available on Github.

GLM-Dialog 是一个拥有 10B 参数的大规模语言模型，使用搜索引擎来访问网络知识进行基于知识的中文对话，在评估方面提出了一种新的评估方法，同时发布了模型检查点和源代码，并通过微信应用程序与用户进行交互，以促进开源模型和可靠对话评估系统的发展。

GLM-Dialog: 知识驱动对话生成的噪声容错预训练

GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation

Knowledge-grounded conversation (KGC) shows excellent potential to deliver an
engaging and informative response. However, existing approaches emphasize
selecting one golden knowledge given a particular dialogue context, overlooking
the one-to-many phenomenon in dialogue. As a result, the existing paradigm
limits the diversity of knowledge selection and generation. To this end, we
establish a multi-reference KGC dataset and propose a series of metrics to
systematically assess the one-to-many efficacy of existing KGC models.
Furthermore, to extend the hypothesis space of knowledge selection to enhance
the mapping relationship between multiple knowledge and multiple responses, we
devise a span-based variational model and optimize the model in a wake-sleep
style with an ameliorated evidence lower bound objective to learn the
one-to-many generalization. Both automatic and human evaluations demonstrate
the efficacy of our approach.

提出一种多参考知识对话模型 (KGC)，为了提高知识选择与生成的多样性，建立了一个多重参考的 KGC 数据集，并提出了一系列测量现有 KGC 模型效力的指标，使用基于跨度的变分模型并以改进的证据下界目标进行优化，以学习一对多泛化关系，该方法在机器和人工评估中均表现出良好的效力。

基于知识的对话生成：对抗性多参照学习中不存在标准答案

There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning

Knowledge-grounded conversation (KGC) shows great potential in building an
engaging and knowledgeable chatbot, and knowledge selection is a key ingredient
in it. However, previous methods for knowledge selection only concentrate on
the relevance between knowledge and dialogue context, ignoring the fact that
age, hobby, education and life experience of an interlocutor have a major
effect on his or her personal preference over external knowledge. Without
taking the personalization issue into account, it is difficult to select the
proper knowledge and generate persona-consistent responses. In this work, we
introduce personal memory into knowledge selection in KGC to address the
personalization issue. We propose a variational method to model the underlying
relationship between one's personal memory and his or her selection of
knowledge, and devise a learning scheme in which the forward mapping from
personal memory to knowledge and its inverse mapping is included in a closed
loop so that they could teach each other. Experiment results show that our
method outperforms existing KGC methods significantly on both automatic
evaluation and human evaluation.

本文介绍了将个人记忆引入知识选择的方法，以解决个性化的问题，采用变分方法建模个人记忆与知识选择之间的关系，并设计了一种学习方式，其中包括了从个人记忆到知识的前向映射和其逆向映射。实验证明，我们的方法在自动评估和人类评估方面都显著优于现有的知识驱动对话方法。