title: WebGLM: Towards An Efficient Web-Enhanced Question Answering System with
    Human Preferences
summary: We present WebGLM, a web-enhanced question-answering system based on the
  General Language Model (GLM). Its goal is to augment a pre-trained large
  language model (LLM) with web search and retrieval capabilities while being
  efficient for real-world deployments. To achieve this, we develop WebGLM with
  strategies for the LLM-augmented retriever, bootstrapped generator, and human
  preference-aware scorer. Specifically, we identify and address the limitations
  of WebGPT (OpenAI), through which WebGLM is enabled with accuracy, efficiency,
  and cost-effectiveness advantages. In addition, we propose systematic criteria
  for evaluating web-enhanced QA systems. We conduct multi-dimensional human
  evaluation and quantitative ablation studies, which suggest the outperformance
  of the proposed WebGLM designs over existing systems. WebGLM with the
  10-billion-parameter GLM (10B) is shown to perform better than the
  similar-sized WebGPT (13B) and even comparably to WebGPT (175B) in human
  evaluation. The code, demo, and data are at
  https://github.com/THUDM/WebGLM.
paper_authors._id: 648927699ab28735f19c358d
paper_authors.name: Xiao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-14T15:18:39.336Z
paper_authors._id: 62f098602ca4d32a7cd87aba
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1659934844914-62f098602ca4d32a7cd87aba.jpeg
paper_authors.fullname: Xiao Liu
paper_authors.type: user
paper_authors.user: ShawLiu
paper_authors._id: 648927699ab28735f19c358e
paper_authors.name: Hanyu Lai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-14T09:29:17.707Z
paper_authors._id: 62ea2d48b493272c269e8f34
paper_authors.avatarUrl: /avatars/89b215dafa503b51ab212a9b63c82aca.svg
paper_authors.fullname: Hanyu Lai
paper_authors.type: user
paper_authors.user: hanyullai
paper_authors._id: 648927699ab28735f19c358f
paper_authors.name: Hao Yu
paper_authors._id: 648927699ab28735f19c3590
paper_authors.name: Yifan Xu
paper_authors._id: 648927699ab28735f19c3591
paper_authors.name: Aohan Zeng
paper_authors._id: 648927699ab28735f19c3592
paper_authors.name: Zhengxiao Du
paper_authors._id: 648927699ab28735f19c3593
paper_authors.name: Peng Zhang
paper_authors._id: 648927699ab28735f19c3594
paper_authors.name: Yuxiao Dong
paper_authors._id: 648927699ab28735f19c3595
paper_authors.name: Jie Tang
paper_title: WebGLM: Towards An Efficient Web-Enhanced Question Answering System with
    Human Preferences
paper_summary: We present WebGLM, a web-enhanced question-answering system based on the
  General Language Model (GLM). Its goal is to augment a pre-trained large
  language model (LLM) with web search and retrieval capabilities while being
  efficient for real-world deployments. To achieve this, we develop WebGLM with
  strategies for the LLM-augmented retriever, bootstrapped generator, and human
  preference-aware scorer. Specifically, we identify and address the limitations
  of WebGPT (OpenAI), through which WebGLM is enabled with accuracy, efficiency,
  and cost-effectiveness advantages. In addition, we propose systematic criteria
  for evaluating web-enhanced QA systems. We conduct multi-dimensional human
  evaluation and quantitative ablation studies, which suggest the outperformance
  of the proposed WebGLM designs over existing systems. WebGLM with the
  10-billion-parameter GLM (10B) is shown to perform better than the
  similar-sized WebGPT (13B) and even comparably to WebGPT (175B) in human
  evaluation. The code, demo, and data are at
  https://github.com/THUDM/WebGLM.
paper_ai_keywords: WebGLM General Language Model (GLM) pre-trained large language model (LLM) retriever bootstrapped generator human preference-aware scorer WebGPT (OpenAI) WebGPT (13B) WebGPT (175B) LLM-augmented retriever human evaluation quantitative ablation studies downstream systems