NEFTune: Noisy Embeddings Improve Instruction Finetuning

3 code implementations • 9 Oct 2023 • Neel Jain, Ping-Yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

We show that language model finetuning can be improved, sometimes dramatically, with a simple augmentation.

Language Modelling

Baseline Defenses for Adversarial Attacks Against Aligned Language Models

1 code implementation • 1 Sep 2023 • Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-Yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein

We find that the weakness of existing discrete optimizers for text, combined with the relatively high costs of optimization, makes standard adaptive attacks more challenging for LLMs.

Bring Your Own Data! Self-Supervised Evaluation for Large Language Models

1 code implementation • 23 Jun 2023 • Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

With the rise of Large Language Models (LLMs) and their ubiquitous deployment in diverse domains, measuring language model behavior on realistic data is imperative.

Chatbot Language Modelling

Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

2 code implementations • NeurIPS 2023 • Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein

In the text-to-image setting, the method creates hard prompts for diffusion models, allowing API users to easily generate, discover, and mix and match image concepts without prior knowledge on how to prompt the model.