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: A naive way of constructing an identifier for our subject is to use an existing word. For example, using the words like “unique” or “special”. One problem is that existing English words tend to have a stronger prior due to occurrence in the training set of text-to-image diffusion models. We generally find increased training time and decreased performance when using such generic words to index our subject, since the model has to both learn to disentangle them from their original meaning and to re-entangle them to reference our subject. This approach can also fail by entangling the meaning of the word with the appearance of our object, for example in the extreme case if the identifier chosen is the adjective “blue” and our subject is grey, colors will be entangled at inference and we will sample a mix of grey and blue subjects (as well as mixes of both).This motivates the need for an identifier that has a weak prior in both the language model and the diffusion model.A hazardous way of doing this is to select random characters in the English language and concatenate them to generate a rare identifier (e.g. “xxy5syt00”). In reality, the tokenizer might tokenize each letter separately, and the prior for the diffusion model is strong for these letters. Specifically, if we sample the model with such an identifier before fine-tuning we will get pictorial depictions of the letters or concepts that are linked to those letters. We often find that these tokens incur the same weaknesses as using common English words to index the subject.

Question: Why shouldn’t an existing word be used as an identifier while fine tuning?
Existing words in the training set of text-to-image diffusion models have stronger priors, hence they shouldn't be employed as identifiers during fine tuning.