← Back to all news
arXiv AI By Damiano Fornasiere, Mirko Bronzi, Spencer Kitts, Alessandro Palmas, Yoshua Bengio, Oliver Richardson

Language models recognize dropout and Gaussian noise applied to their activations

Read the original on arXiv AI →

Language models can detect, localize, and even verbalize differences between dropout and Gaussian noise applied to their activations. In experiments on eight models ranging from 8 B to 36 B, many achieve near‑perfect accuracy in identifying which sentence was perturbed and which type of perturbation was used. The study shows that this ability emerges during pre‑training, correlates with perturbation strength, and can be learned in context, while incorrect labels degrade performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Sep 11

Detectable Only Where It Is Confounded: What Verified Duplication Counts Say About Membership Evidence in Language Models

The paper investigates whether language models can identify sentences from their training data by using exact duplication counts from publicly released corpora for two model families, OLMo‑2 and Pythia. It finds that for typical duplication levels, models show only a weak trace of exposure, with a rank correlation near –0.08, and that strong signals only appear when a sentence appears roughly a thousand times, at which point fame rather than memory dominates. The study also demonstrates that common membership tests can be misleading, as changing a single word does not alter the model’s preference, and that controlling for register can significantly improve detector performance.

By Arman Nik Khah
llms
More like this →
arXiv AI
Sep 4

The Geometry of Ignorance: LLMs Know When to Temper Bayesian Priors

The paper identifies a single direction in the unembedding matrix of large language models that encodes the unigram distribution of the training corpus, acting as a Bayesian prior when the model is uncertain. By projecting the final prediction state onto this direction, the authors derive a per‑token prior loading factor, λ, which decreases as context becomes more informative and decomposes predictions into a tempered prior and a context‑driven likelihood. Experiments across four model families (Llama, Qwen, Gemma, Pythia) show that larger models rely less on the prior in high‑context settings and that manipulating λ can steer predictions toward or away from the unigram prior in KL divergence.

By Toni J. B. Liu, Jiajun Bao, Yizhou Liu, Gurbir Arora, Nicolas Boull\'e, Rapha\"el Sarfati, Christopher J. Earls
llms
More like this →