most citedDo Large Language Models Know What They Don't Know?

7 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2023

Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding

Jianing Wang, Qiushi Sun, Nuo Chen +4

The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios. To r…

cs.CV20231 cited

Exchanging-based Multimodal Fusion with Transformer

Renyu Zhu, Chengcheng Han, Yong Qian +5

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from…

cs.CL20237 cited

Do Large Language Models Know What They Don't Know?

Zhangyue Yin, Qiushi Sun, Qipeng Guo +3

Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their pe…

cs.CL2023

When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario

Chengcheng Han, Liqing Cui, Renyu Zhu +5

Large pre-trained language models (PLMs) have garnered significant attention for their versatility and potential for solving a wide spectrum of natural language processing (NLP) ta…

cs.CL2023

HugNLP: A Unified and Comprehensive Library for Natural Language Processing

Jianing Wang, Nuo Chen, Qiushi Sun +3

In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is desig…