Large Language Models

Constraint Back-translation enhances complex instruction following in LLMs by leveraging inherent constraints in existing datasets for efficient high-quality data creation.

BitStack: Dynamic LLM sizing for variable memory!

Steering large language and diffusion models is made easy and efficient via Activation Transport (ACT)! This novel framework uses optimal transport theory to precisely control model activations, leadi…

AAAR-1.0 benchmark rigorously evaluates LLMs’ ability to assist in four core research tasks, revealing both potential and limitations.

NeuZip dynamically compresses neural network weights, achieving memory-efficient training and inference without performance loss, significantly reducing the memory footprint of large language models.

M2RC-EVAL: A new massively multilingual benchmark for repository-level code completion, featuring fine-grained annotations and a large instruction dataset, enabling better evaluation of code LLMs acro…