31 citations · 54 across the 11 of their papers we have counts for
7 papers · 1 filter
MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
Jie He, Nan Hu, Wanqiu Long +2
Large language models (LLMs) have demonstrated impressive capabilities in various reasoning tasks but face significant challenges with complex, knowledge-intensive multi-hop querie…
Atomic Fact Decomposition Helps Attributed Question Answering
Zhichao Yan, Jiapu Wang, Jiaoyan Chen +3
Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a given question. Retrieval is a widely adopted approach, includ…
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering
Yike Wu, Yi Huang, Nan Hu +4
Recent studies have explored the use of Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA). They typically require…
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
Junjie Wang, Mingyang Chen, Binbin Hu +10
Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enha…
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…
Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs
Nan Hu, Jiaoyan Chen, Yike Wu +6
Attributed Question Answering (AQA) has attracted wide attention, but there are still several limitations in evaluating the attributions, including lacking fine-grained attribution…
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
Yuxia Geng, Jiaoyan Chen, Yuhang Zeng +5
Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they ha…