9 papers
QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction
Vincent Counathe, Ben Athiwaratkun, Christopher De Sa +1
As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential f…
Extractable Memorization From First Principles
A. Feder Cooper, Marika Swanberg, Jamie Hayes +5
Recent work on extractable memorization in LLMs suffers from two contrasting validity problems. Some studies overstate extraction, e.g., relying on sequences too short to distingui…
Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
A. Feder Cooper, Mark A. Lemley, Christopher De Sa +6
Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -…
L: Large Lookup Layers
Albert Tseng, Christopher De Sa
Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP "experts." However, dynamic hard routi…
Model-Preserving Adaptive Rounding
Albert Tseng, Zhaofeng Sun, Christopher De Sa
The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible. To do this tractably, most quantization algorit…
Extracting memorized pieces of (copyrighted) books from open-weight language models
A. Feder Cooper, Mark A. Lemley, Allison Casasola +6
Plaintiffs and defendants in copyright lawsuits over generative AI often make sweeping, opposing claims about the extent to which large language models (LLMs) memorize protected ex…