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Found 6 papers, 2 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.
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.
LAMM: Label Alignment for Multi-Modal Prompt Learning
We conduct experiments on 11 downstream vision datasets and demonstrate that our method significantly improves the performance of existing multi-modal prompt learning models in few-shot scenarios, exhibiting an average accuracy improvement of 2. 31(\%) compared to the state-of-the-art methods on 16 shots.
Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification
Due to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered.
