| | --- |
| | language: |
| | - en |
| | tags: |
| | - glm |
| | - webglm |
| | - thudm |
| | --- |
| | |
| | <h1>WebGLM: Towards An Efficient Web-enhanced Question Answering System with Human Preference</h1> |
| |
|
| | <p align="center"> |
| | 📃 <a href="https://arxiv.org/pdf/2306.07906.pdf" target="_blank">Paper (KDD 2023)</a> |
| | | |
| | 💻 <a href="https://github.com/THUDM/WebGLM" target="_blank">Github Repo</a> |
| | </p> |
| |
|
| | # 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. |
| |
|
