LLM Classification Is Feature Engineering

The article discusses how Large Language Models (LLMs) are often mistakenly referred to as being capable of classification tasks, when in fact they are more accurately described as performing feature extraction. The author argues that LLMs are not inherently classification models, but rather extract features from input data that can be used for classification tasks. This distinction is important, as it highlights the difference between the model's capabilities and its intended application. The author provides examples of how this distinction can be applied to real-world scenarios, including image classification and natural language processing tasks. By understanding LLMs as feature extractors, developers can better utilize their strengths and weaknesses in various applications.