  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.
