1 citations · 1 across the 5 of their papers we have counts for
5 papers
CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning
Chengtao Gan, Zhiqiang Liu, Long Jin +3
Real-world scenarios involve massive heterogeneous structured data (e.g., tables, knowledge graphs), making effective reasoning over such diverse data increasingly important. Unifi…
Self-Correction Distillation for Structured Data Question Answering
Yushan Zhu, Wen Zhang, Long Jin +8
Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have…
TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
Wen Zhang, Long Jin, Yushan Zhu +6
Natural language question answering (QA) over structured data sources such as tables and knowledge graphs have been widely investigated, especially with Large Language Models (LLMs…
Reliable Academic Conference Question Answering: A Study Based on Large Language Model
Zhiwei Huang, Juan Li, Long Jin +6
As the development of academic conferences fosters global scholarly communication, researchers consistently need to obtain accurate and up-to-date information about academic confer…
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Long Jin, Zhen Yao, Mingyang Chen +2
Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with speci…