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Found 7 papers, 4 papers with code
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
On the one hand, retrieving good demonstrations is a non-trivial process in RE, which easily results in low relevance regarding entities and relations.
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors
To this end, we introduce \textsc{Micre} (\textbf{M}eta \textbf{I}n-\textbf{C}ontext learning of LLMs for \textbf{R}elation \textbf{E}xtraction), a new meta-training framework for zero and few-shot RE where an LLM is tuned to do ICL on a diverse collection of RE datasets (i. e., learning to learn in context for RE).
Empirical Analysis of Dialogue Relation Extraction with Large Language Models
However, existing DRE methods still suffer from two serious issues: (1) hard to capture long and sparse multi-turn information, and (2) struggle to extract golden relations based on partial dialogues, which motivates us to discover more effective methods that can alleviate the above issues.
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing.
Ranked #1 on
Knowledge Base Question Answering
on WebQuestionsSP
Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction.
Event-based N-ary Relaiton Extraction
Hypergraph-based N-ary Relaiton Extraction
+3
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level
The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers.
Ranked #1 on
Link Prediction
on Wikipeople
NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs).
Ranked #1 on
Complex Query Answering
on WD50K-QE
