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20212024
most citedWebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus

6 citations · 12 across the 10 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CL2023

Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation

Jiayu Lin, Rong Ye, Meng Han +6

Counter-argument generation -- a captivating area in computational linguistics -- seeks to craft statements that offer opposing views. While most research has ventured into paragra…

cs.CL2023

Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining

Jingcong Liang, Rong Ye, Meng Han +6

The knowledge graph is a structure to store and represent knowledge, and recent studies have discussed its capability to assist language models for various applications. Some varia…

cs.CL2023

IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions

Zhebin Zhang, Xinyu Zhang, Yuanhang Ren +5

Retrieval-Augmented Generation (RAG), by incorporating external knowledge with parametric memory of language models, has become the state-of-the-art architecture for open-domain QA…

cs.CL2023

Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection

Hongjin Qian, Zhicheng Dou, Jiejun Tan +6

Language models (LMs) have revolutionized the way we interact with information, but they often generate nonfactual text, raising concerns about their reliability. Previous methods…

cs.CL2023★ 6 cited

WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus

Hongjing Qian, Yutao Zhu, Zhicheng Dou +7

In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web. In this task, called WebBrai…