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Found 9 papers, 2 papers with code
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.
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.
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction
The in-context learning (ICL) for relational triple extraction (RTE) has achieved promising performance, but still encounters two key challenges: (1) how to design effective prompts and (2) how to select proper demonstrations.
Revisiting Large Language Models as Zero-shot Relation Extractors
On the one hand, we analyze the drawbacks of existing RE prompts and attempt to incorporate recent prompt techniques such as chain-of-thought (CoT) to improve zero-shot RE.
FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework
Recent work for extracting relations from texts has achieved excellent performance.
Balanced Order Batching with Task-Oriented Graph Clustering
In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem.
T-Know: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional Chinese Medicine
T-Know is a knowledge service system based on the constructed knowledge graph of Traditional Chinese Medicine (TCM).
Learning Tree-based Deep Model for Recommender Systems
In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult.
