21 citations · 67 across the 17 of their papers we have counts for
19 papers
AIGS: Generating Science from AI-Powered Automated Falsification
Zijun Liu, Kaiming Liu, Yiqi Zhu +5
Rapid development of artificial intelligence has drastically accelerated the development of scientific discovery. Trained with large-scale observation data, deep neural networks ex…
ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
Zonghan Yang, Peng Li, Ming Yan +3
Language agents have demonstrated autonomous decision-making abilities by reasoning with foundation models. Recently, efforts have been made to train language agents for performanc…
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models
Xuanyu Lei, Zonghan Yang, Xinrui Chen +2
State-of-the-art Large Multi-Modal Models (LMMs) have demonstrated exceptional capabilities in vision-language tasks. Despite their advanced functionalities, the performances of LM…
Towards Unified Alignment Between Agents, Humans, and Environment
Zonghan Yang, An Liu, Zijun Liu +11
The rapid progress of foundation models has led to the prosperity of autonomous agents, which leverage the universal capabilities of foundation models to conduct reasoning, decisio…
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
An Liu, Zonghan Yang, Zhenhe Zhang +6
While Large language models (LLMs) have demonstrated considerable capabilities across various natural language tasks, they often fall short of the performance achieved by domain-sp…
Adversarial Robust Memory-Based Continual Learner
Xiaoyue Mi, Fan Tang, Zonghan Yang +4
Despite the remarkable advances that have been made in continual learning, the adversarial vulnerability of such methods has not been fully discussed. We delve into the adversarial…
Improved Algorithms for Online Rent Minimization Problem Under Unit-Size Jobs
Enze Sun, Zonghan Yang, Yuhao Zhang
We consider the Online Rent Minimization problem, where online jobs with release times, deadlines, and processing times must be scheduled on machines that can be rented for a fixed…
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…
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…
A Closer Look at the Adversarial Robustness of Deep Equilibrium Models
Zonghan Yang, Tianyu Pang, Yang Liu
Deep equilibrium models (DEQs) refrain from the traditional layer-stacking paradigm and turn to find the fixed point of a single layer. DEQs have achieved promising performance on…
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning
Yusheng Su, Chi-Min Chan, Jiali Cheng +9
Parameter-efficient tuning (PET) methods can effectively drive extremely large pre-trained language models (PLMs) by training only minimal parameters. Different PET methods utilize…
Restricted Orthogonal Gradient Projection for Continual Learning
Zeyuan Yang, Zonghan Yang, Peng Li +1
Continual learning aims to avoid catastrophic forgetting and effectively leverage learned experiences to master new knowledge. Existing gradient projection approaches impose hard c…
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…
On Robust Prefix-Tuning for Text Classification
Zonghan Yang, Yang Liu
Recently, prefix-tuning has gained increasing attention as a parameter-efficient finetuning method for large-scale pretrained language models. The method keeps the pretrained model…
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ning Ding, Yujia Qin, Guang Yang +17
Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining sepa…
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation
Rui Jiao, Zonghan Yang, Maosong Sun +1
While synthetic bilingual corpora have demonstrated their effectiveness in low-resource neural machine translation (NMT), adding more synthetic data often deteriorates translation…
Neural Machine Translation: A Review of Methods, Resources, and Tools
Zhixing Tan, Shuo Wang, Zonghan Yang +4
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machi…
Interpolation between Residual and Non-Residual Networks
Zonghan Yang, Yang Liu, Chenglong Bao +1
Although ordinary differential equations (ODEs) provide insights for designing network architectures, its relationship with the non-residual convolutional neural networks (CNNs) is…
Chinese Poetry Generation with a Working Memory Model
Xiaoyuan Yi, Maosong Sun, Ruoyu Li +1
As an exquisite and concise literary form, poetry is a gem of human culture. Automatic poetry generation is an essential step towards computer creativity. In recent years, several…