most citedLLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

12 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20241 cited

CMM-Math: A Chinese Multimodal Math Dataset To Evaluate and Enhance the Mathematics Reasoning of Large Multimodal Models

Wentao Liu, Qianjun Pan, Yi Zhang +7

Large language models (LLMs) have obtained promising results in mathematical reasoning, which is a foundational skill for human intelligence. Most previous studies focus on improvi…

cs.CV2024

UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation

Chaoqun Du, Yulin Wang, Jiayi Guo +3

Test-Time Adaptation (TTA) aims to adapt pre-trained models to the target domain during testing. In reality, this adaptability can be influenced by multiple factors. Researchers ha…

cs.CL20241 cited

Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models

Shunyu Liu, Jie Zhou, Qunxi Zhu +4

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in…

cs.CL202412 cited

LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Junjie Ye, Nuo Xu, Yikun Wang +4

Despite the impressive capabilities of large language models (LLMs), their performance on information extraction tasks is still not entirely satisfactory. However, their remarkable…

cs.CV2023

Skip-Plan: Procedure Planning in Instructional Videos via Condensed Action Space Learning

Zhiheng Li, Wenjia Geng, Muheng Li +4

In this paper, we propose Skip-Plan, a condensed action space learning method for procedure planning in instructional videos. Current procedure planning methods all stick to the st…