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Alexandra Volkova

3 accepted papers

2026

ASIDE: Architectural Separation of Instructions and Data in Language Models

ICLR 2026poster

Despite their remarkable performance, large language models lack elementary safety features, making them susceptible to numerous malicious attacks. In particular, previous work has identified the absence of an intrinsic separation between instructions and data as the root cause of the success of pro…

Cited by 0SourcecodeScholar
2026

Beyond Outliers: A Study of Optimizers Under Quantization

ICLR 2026poster

As new optimizers gain traction and model quantization becomes standard for efficient deployment, a key question arises: how does the choice of optimizer affect model performance in the presence of quantization? Despite progress in both areas, systematic evidence on optimizer–quantization interactio…

Cited by 0SourceScholar
2025

Unified Scaling Laws for Compressed Representations

NeurIPS 2025poster

Scaling laws have shaped recent advances in machine learning by enabling predictable scaling of model performance based on model size, computation, and data volume. Concurrently, the rise in computational cost for AI has motivated model compression techniques, notably quantization and sparsification…

Cited by 0SourceScholar