7 citations · 19 across the 4 of their papers we have counts for
4 papers · 1 filter
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Tu Vu, Mohit Iyyer, Xuezhi Wang +8
Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world. In this work, we perform a detail…
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?
C. Daniel Freeman, Laura Culp, Aaron Parisi +27
We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic…
UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretraining
Hyung Won Chung, Noah Constant, Xavier Garcia +4
Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systema…
Reducing Retraining by Recycling Parameter-Efficient Prompts
Brian Lester, Joshua Yurtsever, Siamak Shakeri +1
Parameter-efficient methods are able to use a single frozen pre-trained large language model (LLM) to perform many tasks by learning task-specific soft prompts that modulate model…