100 citations · 189 across the 3 of their papers we have counts for
3 papers
The False Promise of Imitating Proprietary LLMs
Arnav Gudibande, Eric Wallace, Charlie Snell +5
An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Inst…
Poisoning Language Models During Instruction Tuning
Alexander Wan, Eric Wallace, Sheng Shen +1
Instruction-tuned LMs such as ChatGPT, FLAN, and InstructGPT are finetuned on datasets that contain user-submitted examples, e.g., FLAN aggregates numerous open-source datasets and…
Extracting Training Data from Diffusion Models
Nicholas Carlini, Jamie Hayes, Milad Nasr +6
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images. In this w…