10 citations · 15 across the 6 of their papers we have counts for
6 papers
Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matching
Jianfei Zhang, Bei Li, Jun Bai +4
In-Context Learning (ICL) empowers Large Language Models (LLMs) for rapid task adaptation without Fine-Tuning (FT), but its reliance on demonstration selection remains a critical c…
Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment
Jianfei Zhang, Jun Bai, Bei Li +4
Aligning Large Language Models (LLMs) with general human preferences has been proved crucial in improving the interaction quality between LLMs and human. However, human values are…
Learning to Adapt to Low-Resource Paraphrase Generation
Zhigen Li, Yanmeng Wang, Rizhao Fan +3
Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora. However, transferring a paraphrasing model to another domain encounters t…
Why Batch Normalization Damage Federated Learning on Non-IID Data?
Yanmeng Wang, Qingjiang Shi, Tsung-Hui Chang
As a promising distributed learning paradigm, federated learning (FL) involves training deep neural network (DNN) models at the network edge while protecting the privacy of the edg…
Improving Variational Autoencoders with Density Gap-based Regularization
Jianfei Zhang, Jun Bai, Chenghua Lin +2
Variational autoencoders (VAEs) are one of the powerful unsupervised learning frameworks in NLP for latent representation learning and latent-directed generation. The classic optim…
Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval
Yanmeng Wang, Jun Bai, Ye Wang +5
Dual-Encoders is a promising mechanism for answer retrieval in question answering (QA) systems. Currently most conventional Dual-Encoders learn the semantic representations of ques…