37 citations · 37 across the 13 of their papers we have counts for
13 papers
FlowSDR: Sufficient Dimension Reduction via Conditional Normalizing Flows
Yuexiao Dong, Kenichiro Mcalinn, Edoardo Airoldi +1
Sufficient dimension reduction (SDR) seeks a low-dimensional linear projection of predictors that preserves the conditional distribution of the response. Existing methods target th…
Revise, Don't Freeze: Sampler-Matched Training for Self-Correcting Masked Diffusion Language Models
Longxuan Yu, Shaorong Zhang, Yu Fu +3
Masked diffusion language models (MDLMs) re-predict every position at each denoising step, but standard samplers commit tokens once revealed, leaving this revision capability unuse…
DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs
Longxuan Yu, Yunshu Wu, Yu Fu +5
Discrete Masked diffusion language models generate text by iterative parallel decoding, but few-step decoding suffers from a tradeoff between length and quality: with a fixed step…
Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning
Shangjian Yin, Yu Fu, Yue Dong +1
Reinforcement learning with verifiable rewards (RLVR) is widely used to improve the reasoning capability of language models, since model-generated answers can be easily verified by…
Reducing the Safety Tax in LLM Safety Alignment with On-Policy Self-Distillation
Yu Fu, Longxuan Yu, Haz Sameen Shahgir +4
Safety alignment often improves robustness to harmful queries at the cost of reasoning ability, a tradeoff known as the safety tax. A common cause is distributional mismatch: super…
MT-JailBench: A Modular Benchmark for Understanding Multi-Turn Jailbreak Attacks
Xinkai Zhang, Zhipeng Wei, Huanli Gong +4
Multi-turn jailbreaks exploit the ability of large language models to accumulate and act on conversational context. Instead of stating a harmful request directly, an attacker can g…
Thinking Out of Order: When Output Order Stops Reflecting Reasoning Order in Diffusion Language Models
Longxuan Yu, Yu Fu, Shaorong Zhang +4
Autoregressive (AR) language models enforce a fixed left-to-right generation order, creating a fundamental limitation when the required output structure conflicts with natural reas…
MACE: A Hybrid LLM Serving System with Colocated SLO-aware Continuous Retraining Alignment
Yufei Li, Yu Fu, Yue Dong +1
Large language models (LLMs) deployed on edge servers are increasingly used in latency-sensitive applications such as personalized assistants, recommendation, and content moderatio…
TRAWL: Tensor Reduced and Approximated Weights for Large Language Models
Yiran Luo, Het Patel, Yu Fu +4
Recent research has shown that pruning large-scale language models for inference is an effective approach to improving model efficiency, significantly reducing model weights with m…
Cross-Task Defense: Instruction-Tuning LLMs for Content Safety
Yu Fu, Wen Xiao, Jia Chen +4
Recent studies reveal that Large Language Models (LLMs) face challenges in balancing safety with utility, particularly when processing long texts for NLP tasks like summarization a…
Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study
Yufei Li, Simin Chen, Yanghong Guo +3
Large Language Models (LLMs) have been widely employed in programming language analysis to enhance human productivity. Yet, their reliability can be compromised by various code dis…
Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack
Yu Fu, Yufei Li, Wen Xiao +2
Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety…
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks
Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu +3
Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security…