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Found 4 papers, 4 papers with code
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning.
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
To evaluate the performance of state-of-the-art RC models on the CORE dataset, we conduct experiments in the few-shot domain adaptation setting.
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques
This paper investigates the transferability of debiasing techniques across different languages within multilingual models.
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA
The performance of In-Context Learning depends heavily on the selection procedure of the supporting exemplars, particularly in the case of HybridQA, where considering the diversity of reasoning chains and the large size of the hybrid contexts becomes crucial.
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