7 citations · 9 across the 9 of their papers we have counts for
10 papers
Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning
Zicheng Zhao, Yu Lan, Chengzhengxu Li +2
Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks. While episodic memory mechanisms mitigate these is…
DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection
Guoxin Ma, Xiaoming Liu, Hongyang Chen +6
Detecting machine-generated text has become a critical challenge amid the rapid advancement of LLMs, yet existing detectors degrade severely under domain shift. Through systematic…
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression
Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang +5
Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance. Existing w…
MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Shengchao Liu, Xiaoming Liu, Chengzhengxu Li +4
Large Language Models have shown growing ability to generate fluent and coherent texts that are highly similar to the writing style of humans. Current detectors for Machine-Generat…
Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language Models
Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang +4
Recent advances in prompt optimization have notably enhanced the performance of pre-trained language models (PLMs) on downstream tasks. However, the potential of optimized prompts…
StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation
Xiaoming Liu, Chen Liu, Zhaohan Zhang +4
Large language models have shown their ability to become effective few-shot learners with prompting, revolutionizing the paradigm of learning with data scarcity. However, this appr…
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better
Shengchao Liu, Xiaoming Liu, Yichen Wang +5
The burgeoning generative capabilities of large language models (LLMs) have raised growing concerns about abuse, demanding automatic machine-generated text detectors. DetectGPT, a…
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning
Chengzhengxu Li, Xiaoming Liu, Yichen Wang +3
Prompt-based pre-trained language models (PLMs) paradigm have succeeded substantially in few-shot natural language processing (NLP) tasks. However, prior discrete prompt optimizati…
Robust Approximate Dynamic Programming for Large-scale Unit Commitment with Energy Storages
Yu Lan, Qiaozhu Zhai, Xiaoming Liu +1
The multistage robust unit commitment (UC) is of paramount importance for achieving reliable operations considering the uncertainty of renewable realizations. The typical affine de…
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning
Xiaoming Liu, Zhaohan Zhang, Yichen Wang +3
Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which exce…