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Understanding Catastrophic Forgetting in Language Models via Implicit Inference
Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan
We lack a systematic understanding of the effects of fine-tuning (via methods such as instruction-tuning or reinforcement learning from human feedback), particularly on tasks outsi…
An Explanation of In-context Learning as Implicit Bayesian Inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang +1
Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt c…
Robust Encodings: A Framework for Combating Adversarial Typos
Erik Jones, Robin Jia, Aditi Raghunathan +1
Despite excellent performance on many tasks, NLP systems are easily fooled by small adversarial perturbations of inputs. Existing procedures to defend against such perturbations ar…
Certified Robustness to Adversarial Word Substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel +1
State-of-the-art NLP models can often be fooled by adversaries that apply seemingly innocuous label-preserving transformations (e.g., paraphrasing) to input text. The number of pos…