1 citations · 1 across the 10 of their papers we have counts for
11 papers
DocNavRAG: Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answering
Dongyang Xie, Yao Tian, Hao Zhang +5
Answering complex questions over large document collections requires assembling complementary evidence across sections and documents. GraphRAG offers structured retrieval but typic…
Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering
Guixin Su, Qiankun Pi, Mayi Xu +5
Table Question Answering (TableQA) aims to reason over tables to answer user queries. Existing research treats all questions uniformly and evaluates solely through overall accuracy…
AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models
Hankun Kang, Di Lin, Zhirong Liao +6
With the global proliferation of Large Language Models (LLMs), cultural safety, defined as the ability to generate respectful and appropriate responses across diverse cultures, bec…
Beyond Static Snapshots: Dynamic Modeling and Forecasting of Group-Level Value Evolution with Large Language Models
Qiankun Pi, Guixin Su, Jinliang Li +5
Social simulation is critical for mining complex social dynamics and supporting data-driven decision making. LLM-based methods have emerged as powerful tools for this task by lever…
Can a Small Model Learn to Look Before It Leaps? Dynamic Learning and Proactive Correction for Hallucination Detection
Zepeng Bao, Shen Zhou, Qiankun Pi +5
Hallucination in large language models (LLMs) remains a critical barrier to their safe deployment. For hallucination detection to be practical in real-world scenarios, the use of e…
A Survey on Training-free Alignment of Large Language Models
Birong Pan, Yongqi Li, Weiyu Zhang +6
The alignment of large language models (LLMs) aims to ensure their outputs adhere to human values, ethical standards, and legal norms. Traditional alignment methods often rely on r…
Format as a Prior: Quantifying and Analyzing Bias in LLMs for Heterogeneous Data
Jiacheng Liu, Mayi Xu, Qiankun Pi +5
Large Language Models (LLMs) are increasingly employed in applications that require processing information from heterogeneous formats, including texts, tables, infoboxes, and knowl…
NeuronTune: Fine-Grained Neuron Modulation for Balanced Safety-Utility Alignment in LLMs
Birong Pan, Jianhao Chen, Mayi Xu +4
Ensuring robust safety alignment while preserving utility is critical for the reliable deployment of Large Language Models (LLMs). However, current techniques fundamentally suffer…
Privacy-protected Retrieval-Augmented Generation for Knowledge Graph Question Answering
Yunfeng Ning, Mayi Xu, Jintao Wen +5
LLMs often suffer from hallucinations and outdated or incomplete knowledge. RAG is proposed to address these issues by integrating external knowledge like that in KGs into LLMs. Ho…
Origin-Destination Demand Prediction: An Urban Radiation and Attraction Perspective
Xuan Ma, Zepeng Bao, Ming Zhong +5
In recent years, origin-destination (OD) demand prediction has gained significant attention for its profound implications in urban development. Existing data-driven deep learning m…
A Unified and Scalable Algorithm Framework of User-Defined Temporal -Core Query
Ming Zhong, Junyong Yang, Yuanyuan Zhu +3
Querying cohesive subgraphs on temporal graphs (e.g., social network, finance network, etc.) with various conditions has attracted intensive research interests recently. In this pa…