activity
20232026
most citedSurvey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity

55 citations · 55 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2026

From Events to Trending: A Multi-Stage Hotspots Detection Method Based on Generative Query Indexing

Kaichun Wang, Yanguang Chen, Ting Zhang +7

LLM-based conversational systems have become a popular gateway for information access, yet most existing chatbots struggle to handle news-related trending queries effectively. To i…

cs.CL2024

Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability

Xiangsen Chen, Xuming Hu, Nan Tang

Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks since it enables large language models (LLMs) to incor…

cs.CL2024

UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models

Boyang Xue, Fei Mi, Qi Zhu +6

Despite demonstrating impressive capabilities, Large Language Models (LLMs) still often struggle to accurately express the factual knowledge they possess, especially in cases where…

cs.CL2024

Reasoning Factual Knowledge in Structured Data with Large Language Models

Sirui Huang, Yanggan Gu, Xuming Hu +3

Large language models (LLMs) have made remarkable progress in various natural language processing tasks as a benefit of their capability to comprehend and reason with factual knowl…

cs.CL2023★ 55 cited

Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity

Cunxiang Wang, Xiaoze Liu, Yuanhao Yue +13

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outpu…