From the 1 of 20 linked papers with an AI index.
20 papers
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…
Segmenting Human-LLM Co-authored Text via Change Point Detection
Mengchu Li, Jin Zhu, Jinglai Li +1
The paper introduces algorithms that locate human-written versus LLM-generated segments within a mixed text by treating the problem as a change‑point detection task, and provides t…
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…
Counterfactually Safe Reinforcement Learning
Jingyi Li, Peng Wu, Chengchun Shi
Reinforcement learning algorithms are generally designed to maximize the expected return across a population. However, a policy that is optimal on average may be suboptimal for cer…
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…
Perturbation is All You Need for Extrapolating Language Models
Zetai Cen, Jin Zhu, Xinwei Shen +1
This paper develops a statistical theory of extrapolation for large language models, by reinterpreting them through pre-post-additive noise models. In contrast to the standard auto…