The term language model fine-tuning comes from the fields of artificial intelligence, digital transformation and automation. This is a process in which an existing language model such as GPT-3 or GPT-4 is specifically retrained in order to better adapt it to specific tasks or specialist areas.
Imagine that a language model is like a very well-trained interpreter who speaks many languages but does not yet have a perfect command of your specific corporate language or industry topics. Through language model fine-tuning, this "interpreter" learns exactly the words, phrases and content that are important in your company or industry.
For example, an insurance company wants to introduce a chatbot that not only understands customer enquiries, but also answers them correctly and with legal certainty. With the help of language model fine-tuning, the AI model is retrained with insurance conditions and typical customer questions. This makes the chatbot much more accurate and helpful.
Language model fine-tuning makes artificial intelligence even more precise and efficient for specific use cases and ensures a better user experience.
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