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 use the same pre-processing as in earlier work Johnson and Zhang (2017); McCann et al. (2017). In addition, to allow the language model to capture aspects that might be relevant for classification, we add special tokens for upper-case words, elongation, and repetition. We propose Universal Language Model Finetuning (ULMFiT), which pretrains a language model (LM) on a large general-domain corpus and fine-tunes it on the target task using novel techniques. The method is universal in the sense that it meets these practical criteria: 1) It works across tasks varying in document size, number, and label type; 2) it uses a single architecture and training process; 3) it requires no custom feature engineering or preprocessing; and 4) it does not require additional in-domain documents or labels

Question: Why does the proposed model need advanced preprocessing and feature generation to function well?
The proposed model is universal meaning that it does not require custom feature engineering or preprocessing. For example, the pre-processing is taken from earlier work and only adds special tokens to capture relevant aspects for classification.