Service Composition in the ChatGPT Era

ChatGPT recently attracted vast attention in and outside the research community for its conversational abilities that mimic human ones exceptionally well. At the heart of systems like ChatGPT are Large Language Models (LLM). These models, rooted in deep neural networks, have the ability to predict the next textual token in a series of tokens based on statistical occurrences in extremely large data sets [1]. When the models are sufficiently big and well-tuned, one observes the "unreasonable effectiveness of data" [2] in how the system generates perfectly intelligible and believable sentences. Such ability to have human-like conversations with a software system is both stunning for the quality of the conversation and mind-blowing in terms of the potential impact on society and the job market in particular [3, 4].