WebGLM: Towards An Efficient Web-Enhanced Question Answering System with
  Human Preferences
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.
648927699ab28735f19c358d
Xiao Liu
claimed_verified
2023-06-14T15:18:39.336Z
62f098602ca4d32a7cd87aba
https://cdn-avatars.huggingface.co/v1/production/uploads/1659934844914-62f098602ca4d32a7cd87aba.jpeg
Xiao Liu
user
ShawLiu
648927699ab28735f19c358e
Hanyu Lai
claimed_verified
2023-06-14T09:29:17.707Z
62ea2d48b493272c269e8f34
/avatars/89b215dafa503b51ab212a9b63c82aca.svg
Hanyu Lai
user
hanyullai
648927699ab28735f19c358f
Hao Yu
648927699ab28735f19c3590
Yifan Xu
648927699ab28735f19c3591
Aohan Zeng
648927699ab28735f19c3592
Zhengxiao Du
648927699ab28735f19c3593
Peng Zhang
648927699ab28735f19c3594
Yuxiao Dong
648927699ab28735f19c3595
Jie Tang
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with
  Human Preferences
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.
WebGLM is a web-enhanced question-answering system that improves on WebGPT by integrating web search, retrieval, and human preference to achieve better accuracy, efficiency, and cost-effectiveness.
GLM large language model LLM web search retrieval bootstrapped generator human preference-aware scorer WebGPT multi-dimensional human evaluation quantitative ablation studies