9 citations · 16 across the 7 of their papers we have counts for
7 papers
Pluggable Neural Machine Translation Models via Memory-augmented Adapters
Yuzhuang Xu, Shuo Wang, Peng Li +4
Although neural machine translation (NMT) models perform well in the general domain, it remains rather challenging to control their generation behavior to satisfy the requirement o…
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models
Qinhong Zhou, Zonghan Yang, Peng Li +1
Conventional knowledge distillation (KD) methods require access to the internal information of teachers, e.g., logits. However, such information may not always be accessible for la…
Learning to Relate to Previous Turns in Conversational Search
Fengran Mo, Jian-Yun Nie, Kaiyu Huang +4
Conversational search allows a user to interact with a search system in multiple turns. A query is strongly dependent on the conversation context. An effective way to improve retri…
Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural Dynamics
Zonghan Yang, Peng Li, Tianyu Pang +1
Deep equilibrium (DEQ) models replace the multiple-layer stacking of conventional deep networks with a fixed-point iteration of a single-layer transformation. Having been demonstra…
Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks
Zhicheng Guo, Sijie Cheng, Yile Wang +2
Retrieval-augmented methods have received increasing attention to support downstream tasks by leveraging useful information from external resources. Recent studies mainly focus on…
Black-box Prompt Tuning with Subspace Learning
Yuanhang Zheng, Zhixing Tan, Peng Li +1
Black-box prompt tuning employs derivative-free optimization algorithms to learn prompts within low-dimensional subspaces rather than back-propagating through the network of Large…
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Zonghan Yang, Xiaoyuan Yi, Peng Li +2
Warning: this paper contains model outputs exhibiting offensiveness and biases. Recently pre-trained language models (PLMs) have prospered in various natural language generation (N…