You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We highlight five main observations regarding the results. (1) OPT LMs show a much lower EL10 and MA than GPT-NEO LMs, confirming that deduplicating the pretraining corpora is indeed helpful for mitigating privacy risks. (2) NEO + DPD+ enables effective protection against extraction attacks demonstrated via the lowest EL and MA score; however, it brings severe degradation of generation capabilities measured via the Average F1 score of the 4 dialogue generation tasks. (3) NEO + UL+ results in severe degradation of both classification and dialogue tasks for the 125M, only severe degradation of dialogue tasks for 1.3B LM while for the 2.7B LMs, it enables retaining most of its previous capabilities. (4) While the LMs scale to larger sizes, it takes fewer epochs for the target sequences to be forgotten. Together with (3), this implies that larger LMs are strong unlearners. (5) While NEO + UL+ provides stronger privacy protection than OPT without sacrificing its performance from NEO for the 2.7B LM, it is much more computationally efficient (3,500,000x) than re-training the underlying LM, which is required for all data preprocessing approaches.

Question: What happens when we perform unlearning for really big LMs?
Larger LMs are stronger unlearners because they take fewer epochs for forgetting specific target token sequences and retains most of its previous capabilities compared to smaler LMs.