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

Paper recorded by Signals 4 on 2026-09-18 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.AI · 人工智能 · first seen 2026-09-21

Abstract

Detecting pretraining data in large language models is challenging because high likelihood can reflect either training exposure or strong generalization. In the joint space of prediction loss and predictive entropy, a likelihood-only detector uses a horizontal boundary and can mistake predictable non-members for members. Motivated by this, we introduce an inclined boundary that evaluates predictio

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#13 most recent of 320 cs.AI papers we have recorded · ↑ newer: What Should We Ask Next? Retrieval-Aware Question Learning under Parti · ↓ older: Benchmarking the Explanatory Quality of Open-Weight Vision-Language Mo
Cite this page: Detecting Pretraining Data in Large Language Models from a Free-Energy Perspective: the #13 most recent of 320 cs.AI papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/detecting-pretraining-data-in-large-language-models-from-a-free-energy-perspecti.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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