English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Scaling language models have revolutionized widespread NLP tasks, yet littlecomprehensively explored few-shot relation extraction with large languagemodels. In this paper, we investigate principal methodologies, in-contextlearning and data generation, for few-shot relation extraction via GPT-3.5through exhaustive experiments. To enhance few-shot performance, we furtherpropose task-related instructions and schema-constrained data generation. Weobserve that in-context learning can achieve performance on par with previousprompt learning approaches, and data generation with the large language modelcan boost previous solutions to obtain new state-of-the-art few-shot results onfour widely-studied relation extraction datasets. We hope our work can inspirefuture research for the capabilities of large language models in few-shotrelation extraction. Code is available inhttps://github.com/zjunlp/DeepKE/tree/main/example/llm.

How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?