THUDM/WebGLM

Health v2 · maintenance only

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1602 stars · 131 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

WebGLM is an efficient web-enhanced question answering system (KDD 2023) that combines a large language model with web search retrieval and human preference alignment (RLHF). It ships pretrained models (WebGLM-10B, WebGLM-2B), a QA dataset, and an official Python implementation with a demo.

Use cases

• answer questions using up-to-date web search results with an llm
• build a retrieval-augmented question answering system
• run a web-enhanced chatbot grounded in search results
• fine-tune or evaluate llms with human preference data for qa
• use the webglm-qa dataset for research on web-enhanced qa
• deploy an open-source alternative to web-connected chatgpt

When to choose

• you need an open-source, research-backed web-enhanced QA pipeline with pretrained checkpoints
• you want to reproduce or extend KDD 2023 research on retrieval-augmented QA with RLHF
• you need a smaller (2B) or larger (10B) model for grounded question answering on limited GPU hardware

When to avoid

• you need a production-grade, actively maintained RAG framework rather than a research codebase
• you want a general-purpose chat assistant without web retrieval dependencies (e.g., SerpAPI key required)
• you need a lightweight plug-and-play library rather than a full system with model checkpoints

Facets

application · maturity active

rag llm-inference machine-learning nlp search-engine large-language-models artificial-intelligence python web-enhanced-qa rlhf question-answering chatglm research-project kdd-2023 pretrained-models retrieval-augmented-generation natural-language-processing gpu docker linux

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markdown · JSON · MCP: product_card(name="THUDM/WebGLM")