Task Contamination: Language Models May Not Be Few-Shot Anymore
Changmao Li Jeffrey Flanigan
Large language models (LLMs) offer impressive performance in various
zero-shot and few-shot tasks. However, their success in zero-shot and few-shot
settings may be affected by task contamination, a potential limitation that has
not been thoroughly examined. This paper investigates how zero-shot and
few-shot performance of LLMs has changed chronologically over time. Utilizing
GPT-3 series models and several other recent open-sourced LLMs, and controlling
for dataset difficulty, we find that on datasets released before the LLM
training data creation date, LLMs perform surprisingly better than on datasets
released after. This strongly indicates that, for many LLMs, there exists task
contamination on zero-shot and few-shot evaluation for datasets released prior
to the LLMs' training data creation date. Additionally, we utilize training
data inspection, task example extraction, and a membership inference attack,
which reveal further evidence of task contamination. Importantly, we find that
for classification tasks with no possibility of task contamination, LLMs rarely
demonstrate statistically significant improvements over simple majority
baselines, in both zero and few-shot settings.