To formulate a proper answer or summary to the user, the model interprets the given prompt and produces novel sentences. In almost all the tasks mentioned above, the language model generates text using a language model. Other examples include automatic summarization to extract the most important information from a given text and question-answering which forms the basis of the modern chatbot. DeepL is another example with very good state-of-the-art translation capabilities with new languages being added every year. Whereas up to about five years ago a translation done by Google was still synonymous with "very clunky incoherent piece of text", in recent years these neural machine translations have become very accurate. Machine translation is another well-known application of language modeling and is advancing rapidly. Such sentiment analysis is used to automatically create movie scores on IMDB or product reviews on Amazon, based on how the users write their comments. By interpreting texts from customers, the program can determine whether a text is positive, neutral or negative in tone. What tasks can language models perform that make our lives easier? One important function is classifying text. Of course, these translations and answers in practice are not yet perfect, but they have come a long way since Eliza. Also think of using Google translate to convert your mails to the desired language or letting your phone auto-complete your sentence while you type. Whether you are shopping online or checking your balance, chatbots are ready to answer all your questions. Today, we use applications daily that work with - fortunately a lot more sophisticated - language models. Although it's a simple model, Eliza could identify keywords from the user input and match a pre-programmed answer. The language model underlying Eliza uses pattern recognition based on statistics to generate responses. Eliza was the first program to apply natural language processing (NLP).
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