WebGLM: Towards An Efficient Web-Enhanced Question Answering System with
  Human Preferences
Authors: name: Xiao Liu; name: Hanyu Lai; name: Hao Yu; name: Yifan Xu; name: Aohan Zeng; name: Zhengxiao Du; name: Peng Zhang; name: Yuxiao Dong; name: Jie Tang
Published: 2023-06-13 16:57:53

Abstract: 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.