5 papers
End-to-End Context Compression at Scale
Ang Li, Sean McLeish, Haozhe Chen +12
Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degra…
LiveBench: A Challenging, Contamination-Limited LLM Benchmark
Colin White, Samuel Dooley, Manley Roberts +15
Test set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render ben…
Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks
Ang Li, Yin Zhou, Vethavikashini Chithrra Raghuram +2
A high volume of recent ML security literature focuses on attacks against aligned large language models (LLMs). These attacks may extract private information or coerce the model in…
Exploiting Sparsity for Long Context Inference: Million Token Contexts on Commodity GPUs
Ryan Synk, Monte Hoover, John Kirchenbauer +6
There is growing demand for performing inference with hundreds of thousands of input tokens on trained transformer models. Inference at this extreme scale demands significant compu…
A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers
Alex Stein, Samuel Sharpe, Doron Bergman +5
Many real-world applications of tabular data involve using historic events to predict properties of new ones, for example whether a credit card transaction is fraudulent or what ra…