2.7k citations · 2.7k across the 7 of their papers we have counts for
8 papers
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming
Suyu Ge, Chunting Zhou, Rui Hou +5
Red-teaming is a common practice for mitigating unsafe behaviors in Large Language Models (LLMs), which involves thoroughly assessing LLMs to identify potential flaws and addressin…
Effective Long-Context Scaling of Foundation Models
Wenhan Xiong, Jingyu Liu, Igor Molybog +18
We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with l…
Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone +65
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our f…
Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization
Anastasia Razdaibiedina, Yuning Mao, Rui Hou +4
Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft pro…
MMViT: Multiscale Multiview Vision Transformers
Yuchen Liu, Natasha Ong, Kaiyan Peng +8
We present Multiscale Multiview Vision Transformers (MMViT), which introduces multiscale feature maps and multiview encodings to transformer models. Our model encodes different vie…
SVT: Supertoken Video Transformer for Efficient Video Understanding
Chenbin Pan, Rui Hou, Hanchao Yu +3
Whether by processing videos with fixed resolution from start to end or incorporating pooling and down-scaling strategies, existing video transformers process the whole video conte…