2 citations · 3 across the 12 of their papers we have counts for
13 papers
Does Deeper Reasoning Compromise Alignment? Revealing and Mitigating of Alignment Collapse in Large Reasoning Models
Yu-Hang Wu, Yu-Jie Xiong, Henghua Zhang +3
The emergence of Chain-of-Thought (CoT) has established a robust foundation for Large Reasoning Models (LRMs). While deep reasoning is widely believed to enhance safety alignment,…
Cognitive Loop of Thought: Reversible Hierarchical Markov Chain for Efficient Mathematical Reasoning
Jia-Chen Zhang, Yu-Jie Xiong, Zheng Zhou
Multi-step Chain-of-Thought (CoT) has significantly advanced the mathematical reasoning capabilities of LLMs by leveraging explicit reasoning steps. However, the widespread adoptio…
Expert Pyramid Tuning: Efficient Parameter Fine-Tuning for Expertise-Driven Task Allocation
Jia-Chen Zhang, Zhen-Wei Yan, Yu-Jie Xiong +1
Parameter-Efficient Fine-Tuning (PEFT) has become a dominant paradigm for deploying LLMs in multi-task scenarios due to its extreme parameter efficiency. While Mixture-of-Experts (…
Multi-Sample Anti-Aliasing and Constrained Optimization for 3D Gaussian Splatting
Zheng Zhou, Jia-Chen Zhang, Yu-Jie Xiong +1
Recent advances in 3D Gaussian splatting have significantly improved real-time novel view synthesis, yet insufficient geometric constraints during scene optimization often result i…
Wavelet Mixture of Experts for Time Series Forecasting
Zheng Zhou, Yu-Jie Xiong, Jia-Chen Zhang +2
The field of time series forecasting is rapidly advancing, with recent large-scale Transformers and lightweight Multilayer Perceptron (MLP) models showing strong predictive perform…
Subject or Style: Adaptive and Training-Free Mixture of LoRAs
Jia-Chen Zhang, Yu-Jie Xiong
Fine-tuning models via Low-Rank Adaptation (LoRA) demonstrates remarkable performance in subject-driven or style-driven generation tasks. Studies have explored combinations of diff…
Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
Zheng Zhou, Yu-Jie Xiong, Jia-Chen Zhang +3
The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced Novel View Synthesis (NVS) through explicit scene representation, enabling real-time photorealistic renderi…
CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generation
Jia-Chen Zhang, Zheng Zhou, Yu-Jie Xiong +2
Training data has been proven to be one of the most critical components in training generative AI. However, obtaining high-quality data remains challenging, with data privacy issue…
Sugar-Coated Poison: Benign Generation Unlocks LLM Jailbreaking
Yu-Hang Wu, Yu-Jie Xiong, Hao Zhang +2
With the increasingly deep integration of large language models (LLMs) across diverse domains, the effectiveness of their safety mechanisms is encountering severe challenges. Curre…
Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace
Jia-Chen Zhang, Yu-Jie Xiong, Chun-Ming Xia +2
Large language model (LLM) is considered a milestone towards achieving Artificial General Intelligence (AGI). With its advanced emergent capabilities, it adapt to a wide range of s…
Mixture of Routers
Jia-Chen Zhang, Yu-Jie Xiong, Xi-He Qiu +3
Supervised fine-tuning (SFT) is a milestone in aligning large language models with human instructions and adapting them to downstream tasks. In particular, Low-Rank Adaptation (LoR…
Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting
Dong-Hai Zhu, Yu-Jie Xiong, Jia-Chen Zhang +2
Chain-of-Thought (CoT) Prompting is a dominant paradigm in Large Language Models (LLMs) to enhance complex reasoning. It guides LLMs to present multi-step reasoning, rather than ge…
LoRA : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models
Jia-Chen Zhang, Yu-Jie Xiong, He-Xi Qiu +2
Fine-tuning large language models (LLMs) with high parameter efficiency for downstream tasks has become a new paradigm. Low-Rank Adaptation (LoRA) significantly reduces the number…