544 citations · 564 across the 14 of their papers we have counts for
7 papers · 1 filter
Interpretable LLM-based Table Question Answering
Giang Nguyen, Ivan Brugere, Shubham Sharma +3
Interpretability in Table Question Answering (Table QA) is critical, especially in high-stakes domains like finance and healthcare. While recent Table QA approaches based on Large…
Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective
Rui Yang, Boming Yang, Aosong Feng +7
Knowledge Graphs (KGs) are crucial in the field of artificial intelligence and are widely used in downstream tasks, such as question-answering (QA). The construction of KGs typical…
Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education
Rui Yang, Boming Yang, Sixun Ouyang +6
Knowledge graphs (KGs) are crucial in the field of artificial intelligence and are widely applied in downstream tasks, such as enhancing Question Answering (QA) systems. The constr…
TacoERE: Cluster-aware Compression for Event Relation Extraction
Yong Guan, Xiaozhi Wang, Lei Hou +4
Event relation extraction (ERE) is a critical and fundamental challenge for natural language processing. Existing work mainly focuses on directly modeling the entire document, whic…
KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking
Jiawei Zhang, Chejian Xu, Yu Gai +3
This paper introduces KnowHalu, a novel approach for detecting hallucinations in text generated by large language models (LLMs), utilizing step-wise reasoning, multi-formulation qu…
Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
Rui Yang, Boming Yang, Sixun Ouyang +6
In the domain of Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated promise in text-generation tasks. However, their educational applications, partic…
Better Explain Transformers by Illuminating Important Information
Linxin Song, Yan Cui, Ao Luo +2
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings. Prior methods explain Transformers…