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.
- 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.
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