29 citations · 62 across the 6 of their papers we have counts for
6 papers
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…
HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution
Ehsan Kamalloo, Aref Jafari, Xinyu Zhang +2
The rise of large language models (LLMs) had a transformative impact on search, ushering in a new era of search engines that are capable of generating search results in natural lan…
GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration
Aleksandra Piktus, Odunayo Ogundepo, Christopher Akiki +6
Noticing the urgent need to provide tools for fast and user-friendly qualitative analysis of large-scale textual corpora of the modern NLP, we propose to turn to the mature and wel…
Zero-Shot Listwise Document Reranking with a Large Language Model
Xueguang Ma, Xinyu Zhang, Ronak Pradeep +1
Supervised ranking methods based on bi-encoder or cross-encoder architectures have shown success in multi-stage text ranking tasks, but they require large amounts of relevance judg…
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…
Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval
Jimmy Lin, David Alfonso-Hermelo, Vitor Jeronymo +8
The advent of multilingual language models has generated a resurgence of interest in cross-lingual information retrieval (CLIR), which is the task of searching documents in one lan…