15 papers
Can Tabular Foundation Models Amortize Statistical Inference?
Kai Ye, Shijin Gong, Hongyi Zhou +2
For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regression function, statisticians d…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
Yangjun Lu, Hongyi Zhou, Fabian Spill +3
The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship do…
ELDiff: When Evidential Learning Meets Text-to-Image Diffusion
Qingtao Pan, Kai Ye, Zhihao Dou +2
In multi-object text-to-image (T2I) diffusion, ensuring semantic consistency between textual prompts and generated visual content is crucial for image synthesis. However, such cons…
BASIS: Batchwise Advantage Estimation from Single-Rollout Information Sharing for LLM Reasoning
Shijin Gong, Erhan Xu, Kai Ye +3
Reinforcement learning with verifiable rewards has become a standard recipe for improving the reasoning abilities of large language models. Existing algorithms face a tradeoff betw…
READER: Reasoning-Enhanced AI-Generated Text Detection
Pingfan Su, Kai Ye, Shijin Gong +4
Recent advances in large language models (LLMs) have made it increasingly difficult to distinguish human-written text from AI-generated content. Many existing detectors train super…
Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning
Shijin Gong, Kai Ye, Jin Zhu +3
Recent advances in large language models (LLMs) have increasingly relied on reinforcement learning (RL) to improve their reasoning capabilities. Three types of approaches have been…
Conditional Factuality Controlled LLMs with Generalization Certificates via Conformal Sampling
Kai Ye, Qingtao Pan, Shuo Li
Large language models (LLMs) need reliable test-time control of hallucinations. Existing conformal methods for LLMs typically provide only \emph{marginal} guarantees and rely on a…
Demystifying Group Relative Policy Optimization: Its Policy Gradient is a U-Statistic
Hongyi Zhou, Kai Ye, Erhan Xu +4
Group relative policy optimization (GRPO), a core methodological component of DeepSeekMath and DeepSeek-R1, has emerged as a cornerstone for scaling reasoning capabilities of large…
GMP: A Benchmark for Content Moderation under Co-occurring Violations and Dynamic Rules
Houde Dong, Yifei She, Kai Ye +3
Online content moderation is essential for maintaining a healthy digital environment, and reliance on AI for this task continues to grow. Consider a user comment using national ste…
Learn-to-Distance: Distance Learning for Detecting LLM-Generated Text
Hongyi Zhou, Jin Zhu, Kai Ye +3
Modern large language models (LLMs) such as GPT, Claude, and Gemini have transformed the way we learn, work, and communicate. Yet, their ability to produce highly human-like text r…
A Difference-in-Difference Approach to Detecting AI-Generated Images
Xinyi Qi, Kai Ye, Chengchun Shi +3
Diffusion models are able to produce AI-generated images that are almost indistinguishable from real ones. This raises concerns about their potential misuse and poses substantial c…
AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees
Hongyi Zhou, Jin Zhu, Pingfan Su +4
We study the problem of determining whether a piece of text has been authored by a human or by a large language model (LLM). Existing state of the art logits-based detectors make u…
Doubly Robust Alignment for Large Language Models
Erhan Xu, Kai Ye, Hongyi Zhou +3
This paper studies reinforcement learning from human feedback (RLHF) for aligning large language models with human preferences. While RLHF has demonstrated promising results, many…
Low-Rate Semantic Communication with Codebook-based Conditional Generative Models
Kailang Ye, Mingze Gong, Shuoyao Wang +1
Generative semantic communication models are reshaping semantic communication frameworks by moving beyond pixel-wise optimization to align with human perception. However, many exis…
Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning
Kai Ye, Hongyi Zhou, Jin Zhu +2
Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward…