Should AI models be like Swiss Army knives, versatile and handy in a variety of scenarios? Or do we prefer them as precision tools, finely tuned for specific tasks? In the world of artificial intelligence, and natural language processing specifically, this is an ongoing debate. The question boils down to whether models trained for specific tasks are more effective at these tasks than general models. Task-specific models: specialization and customization In my last blog post , we looked at the rise of personalized LLMs, customized for specific users. Personalized LLMs can be seen as an extreme form of task-specific model. Fans of task-specific models stress that these kinds of models are better suited for tasks involving confidential or proprietary data. This is obviously true. But some people also believe that specialized models necessarily perform better in their specific domains. It may sound logical, but the ans...
Introduction The rise of Large Language Models (LLMs) such as ChatGPT has been revolutionary and is poised to radically change society as we know it. Over the last few months, many companies have started looked into creating their own “personalized LLMs”, tailored with insights derived from their company's specific documentation and data and fine-tuned for specific tasks. It is anticipated that these so-called Leveraged Pre-trained Language Models (LPLMs) will revolutionize various domains like healthcare, finance, and customer service by enabling more intuitive and personalized interactions, enhanced data analysis, and streamlined decision-making processes. While the rest of the early 2020s are poised for a significant integration of LPLMs, we can, in the near future, also look forward to Individualized Language Models (ILMs), tailored to suit individual preferences, needs, and purposes. In an interview with ...