128 citations · 147 across the 4 of their papers we have counts for
4 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…
LIMA: Less Is More for Alignment
Chunting Zhou, Pengfei Liu, Puxin Xu +12
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and re…
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…
Progressive Prompts: Continual Learning for Language Models
Anastasia Razdaibiedina, Yuning Mao, Rui Hou +3
We introduce Progressive Prompts - a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting…