arXiv AI By Beatriz Almeida Felicio

How Divergence Becomes Decision Flips in Compressed Language Models

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arXiv Machine Learning
Jun 25

Internal Data Repetition Destroys Language Models

arXiv:2606. 24998v1 Announce Type: new Abstract: Language models are running out of high-quality training data, and even aggressively deduplicated corpora retain some amount of repetition.

By Jessica Chudnovsky, Joshua Kazdan, Noam Levi, Rylan Schaeffer, Yegor Denisov-Blanch, Bo He, Mehmet Donmez, Sanmi Koyejo, David Donoho
arXiv Machine Learning
Sep 18

Measurement Under Selection: Decoy-Calibrated Failure Audits for Language Models

The paper introduces Janus, a method for validating error patterns in language models by comparing error rates across predefined yes/no properties and using shuffled decoy labels to set significance thresholds. Janus requires that a pattern’s error difference surpasses the decoy-derived threshold and is replicated on held‑out data before reporting. Experiments on a controlled code‑finding task confirm several meaningful error patterns, while on MuSiQue and LongBench v2 Janus reports no confirmed patterns for the tested properties, contrasting with standard shuffling tests that sometimes confirm patterns.

By Vyzantinos Repantis, Ameya Gawde, Harshvardhan Singh