📃 Paper (KDD 2023) | 💻 Github Repo
# Introduction WebGLM aspires to provide an efficient and cost-effective web-enhanced question-answering system using the 10-billion-parameter General Language Model (GLM). It aims to improve real-world application deployment by integrating web search and retrieval capabilities into the pre-trained language model. WebGLM is built by the following parts: - **LLM-augmented Retriever**: Enhances the retrieval of relevant web content to better aid in answering questions accurately. - **Bootstrapped Generator**: Generates human-like responses to questions, leveraging the power of the GLM to provide refined answers. - **Human Preference-aware Scorer**: Estimates the quality of generated responses by prioritizing human preferences, ensuring the system produces useful and engaging content. This repo is the implementation of **Bootstrap Generator**. See our [Github Repo](https://github.com/THUDM/WebGLM) for more detailed usage.