Explain in your own words how a probabilistic model designed for text generation determines the next word in a sequence, given a series of preceding words.

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Language models are probabilistic models that are capable of predicting the next word given the preceding words in a sequence. There is evidence for the ability of language models to model sequential data of fixed length context using feed forward neural networks. 

Conditional language models are also used as a variant of language models where the language model is conditioned on variables other than the preceding words, like the work done by generating product reviews based on sentiment, author, item or category or generating text with emotional context.

Language Models for NLG

A developer is designing a system to automatically generate email responses to customer feedback. The system needs to produce apologetic and helpful text for negative feedback, and enthusiastic, thankful text for positive feedback. Which approach best describes the core requirement for the probabilistic model to perform this task effectively?

Based on the scenario provided, identify the primary limitation of the company's current probabilistic model and propose a specific modification to its design to achieve the desired personalization. Justify your proposal.

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