arXiv AI By Chenye Ke, Zirui Liu, Qi Liu, Yan Zhuang, Jintao Zhang, Zhenya Huang, Shijin Wang

Detecting Pretraining Data in Large Language Models from a Free-Energy Perspective

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

Hugging Face Trending Papers
Jul 6

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure

Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain behavior at the cost of degrading pre-existing capabilities. Standard cross-entropy fine-tuning promotes only the observed label token and leaves unconstrained how probability mass is redistributed over other plausible alternatives, potentially distorting the rich local preference structure learned during pretraining.

arXiv Computation and Language
Aug 27

Unveiling Spectral Mechanisms in Training-Free LLM Text Detection

The paper investigates training‑free detection of machine‑generated text using spectral analysis. It shows that spectral energy correlates with variance in token probability trajectories and that human writing produces characteristic fluctuations, termed "generative vitality." The authors find that spectral signals are strongest for long, continuous, constrained generations, while shorter or mixed texts require additional confidence‑based metrics.

By Haitong Luo, Xuying Meng, Weiyao Zhang, Wenji Zou, Shengfeng Lou, Xuefeng Jiang, Chungang Lin, Yujun Zhang