135 citations · 830 across the 63 of their papers we have counts for
84 papers · 1 filter
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation
Jinyi Hu, Xiaoyuan Yi, Wenhao Li +2
Variational Auto-Encoder (VAE) has been widely adopted in text generation. Among many variants, recurrent VAE learns token-wise latent variables with each conditioned on the preced…
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention
Wenhao Li, Xiaoyuan Yi, Jinyi Hu +2
Recently, powerful Transformer architectures have proven superior in generating high-quality sentences. Nevertheless, these models tend to produce dull high-frequency phrases, seve…
FPT: Improving Prompt Tuning Efficiency via Progressive Training
Yufei Huang, Yujia Qin, Huadong Wang +4
Recently, prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). Despite extensively reducing the number of t…
Exploring Mode Connectivity for Pre-trained Language Models
Yujia Qin, Cheng Qian, Jing Yi +6
Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, st…
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuning
Jing Yi, Weize Chen, Yujia Qin +6
Delta tuning (DET, also known as parameter-efficient tuning) is deemed as the new paradigm for using pre-trained language models (PLMs). Up to now, various DETs with distinct desig…
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP
Yangyi Chen, Hongcheng Gao, Ganqu Cui +4
Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation. However, most work mi…