Service composition in the ChatGPT era

Abstract

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].There is controversy on whether or not such systems manifest forms of artificial intelligence.Researchers at Microsoft, for instance, attribute signs of intelligence to the current fourth version of Generative Pre-trained Transformer (GPT-4), which is in development at the time of writing [5].Some authors have successfully solved Theory of Mind tasks using such tools.Kosinski reports a success rate of 95% using GPT-4 in solving false-belief tasks.Other authors are more careful with the excessive anthropomorphization of ChatGPT-like systems [6,7].What is sure is that the embedding of an LLM into a system makes it a very powerful tool.Of interest to us in this editorial is the LLM capability to generate programs [8] and its potential impact on Service-Oriented Computing and Applications.The problem of automated service composition is central to the field of Service-Oriented Computing and Applications [9].Seamless, unsupervised, automated composition of services available on a network is a potent way to build adaptive information systems.It is the idea that one can execute any task relying on multiple, loosely coupled services, pos-

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