WebGLM：朝向具人類偏好的高效Web增強問答系統前進

摘要

我們提出了WebGLM，這是一個基於通用語言模型（GLM）的網頁增強問答系統。其目標是通過在現有的大型語言模型（LLM）上增加網頁搜索和檢索功能，同時保持在實際部署中的高效性。為了實現這一目標，我們為LLM增強的檢索器、引導生成器和人類偏好感知評分器開發了WebGLM策略。具體來說，我們識別並解決了WebGPT（OpenAI）的限制，從而使WebGLM具有準確性、效率和成本效益方面的優勢。此外，我們提出了評估網頁增強型QA系統的系統性標準。我們進行了多維人類評估和定量消融研究，結果表明所提出的WebGLM設計優於現有系統。WebGLM搭載10億參數的GLM（10B）在人類評估中表現優於相似大小的WebGPT（13B），甚至與WebGPT（175B）相當。代碼、演示和數據可在https://github.com/THUDM/WebGLM找到。

English

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：朝向具人類偏好的高效Web增強問答系統前進

WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences

摘要

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