477 citations · 1.3k across the 32 of their papers we have counts for
11 papers · 1 filter
Supervised Knowledge Makes Large Language Models Better In-context Learners
Linyi Yang, Shuibai Zhang, Zhuohao Yu +8
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded th…
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue +13
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outpu…
A Survey on Evaluation of Large Language Models
Yupeng Chang, Xu Wang, Jindong Wang +13
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to…
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
Yidong Wang, Zhuohao Yu, Zhengran Zeng +10
Instruction tuning large language models (LLMs) remains a challenging task, owing to the complexity of hyperparameter selection and the difficulty involved in evaluating the tuned…
PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou +8
The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this…
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
Linyi Yang, Yaoxiao Song, Xuan Ren +6
Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differ…
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
Hao Chen, Ankit Shah, Jindong Wang +6
Learning with reduced labeling standards, such as noisy label, partial label, and multiple label candidates, which we generically refer to as \textit{imprecise} labels, is a common…
Evaluating Open-QA Evaluation
Cunxiang Wang, Sirui Cheng, Qipeng Guo +7
This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic ev…
Exploring Vision-Language Models for Imbalanced Learning
Yidong Wang, Zhuohao Yu, Jindong Wang +6
Vision-Language models (VLMs) that use contrastive language-image pre-training have shown promising zero-shot classification performance. However, their performance on imbalanced d…
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
Jindong Wang, Xixu Hu, Wenxin Hou +10
ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been don…
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
Hao Chen, Ran Tao, Yue Fan +6
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization pe…