DeepKE VS VLDet

Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5 through exhaustive experiments. To enhance few-shot performance, we further propose task-related instructions and schema-constrained data generation. We observe that in-context learning can achieve performance on par with previous prompt learning approaches, and data generation with the large language model can boost previous solutions to obtain new state-of-the-art few-shot results on four widely-studied relation extraction datasets. We hope our work can inspire future research for the capabilities of large language models in few-shot relation extraction. Code is available in \url{https://github.com/zjunlp/DeepKE/tree/main/example/llm.

We're excited to share our latest work "Learning Object-Language Alignments for Open-Vocabulary Object Detection", which got accepted to ICLR'2023. Here're some resources: arxiv paper: https://arxiv.org/abs/2211.14843 github: https://github.com/clin1223/VLDet The proposed method called **VLDet**, which is a a simple yet effective vision-and-language framework for open-vocabulary object detection. Our key efforts are: 🔥 We introduce an open-vocabulary object detector method to learn object-language alignments directly from image-text pair data. 🔥 We propose to formulate region-word alignments as a set-matching problem and solve it efficiently with the Hungarian algorithm. 🔥 We use all nouns from image-text pairs as our object voccabulary which is strictly following the open-vocabulary setting and extensive experiments on two benchmark datasets, COCO and LVIS, demonstrate our superior performance.