
<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-2B aspires to provide an efficient and cost-effective web-enhanced question-answering system using the 2-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.

{"language": ["en"], "tags": ["glm", "webglm", "thudm"], "inference": false}
transformers pytorch glm feature-extraction webglm thudm custom_code en arxiv:2306.07906 has_space region:us
TAGS
#transformers #pytorch #glm #feature-extraction #webglm #thudm #custom_code #en #arxiv-2306.07906 #has_space #region-us 


<h1>WebGLM: Towards An Efficient Web-enhanced Question Answering System with Human Preference</h1>

<p align="center">
   <a href="URL target="_blank">Paper (KDD 2023)</a> 
  | 
   <a href="URL target="_blank">Github Repo</a>
</p>

# Introduction

WebGLM-2B aspires to provide an efficient and cost-effective web-enhanced question-answering system using the 2-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 for more detailed usage.

# Introduction

WebGLM-2B aspires to provide an efficient and cost-effective web-enhanced question-answering system using the 2-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 for more detailed usage.
TAGS
#transformers #pytorch #glm #feature-extraction #webglm #thudm #custom_code #en #arxiv-2306.07906 #has_space #region-us 
 # Introduction

WebGLM-2B aspires to provide an efficient and cost-effective web-enhanced question-answering system using the 2-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 for more detailed usage.