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Large-scale, general-purpose large language models (LLMs) have achieved remarkable progress by training on vast, heterogeneous datasets and supporting a wide range of language processing tasks. However, empirical studies and industry analyses reveal that these models often fall short in mission-critical and complex applications, lacking precision in delivering contextual relevance, domain understanding, robustness, security, and explainability. Comparative research indicates that customizing general-purpose models through post-training procedures on proprietary data, such as fine-tuning, can yield substantial gains in accuracy, reliability, and performance tailored to domain-specific challenges. Furthermore, strategic shifts in the industry, exemplified by traditionally LLM-bullish companies prioritizing custom-tailored models over merely scaling up model size, underscore that a one-size-fits-all approach is insufficient to address modern enterprises' unique operational, data privacy, and integration demands.
This Special Issue aims to offer a platform for disseminating research and detailed case studies that rigorously explore novel methodologies for designing, implementing, and evaluating custom (also termed targeted, tailored, focused) AI models and composite AI systems (built upon custom AI models) for mission-critical and complex applications, including data collection, data generation, and data validation. This collection demonstrates how bespoke AI solutions can deliver enhanced performance, secure operations, and sustainable efficiency in high-stakes, domain-specific environments by emphasizing customization, advanced model adaptation techniques, and robust integration strategies.
Topics of interest include, but are not limited to:
For author information and guidelines on submission criteria, visit the Author’s Information page. Please submit papers through the IEEE Author Portal and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts. If requested, abstracts should be sent by email to the guest editors directly.
In addition to submitting your paper to IEEE Internet Computing, you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE's data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission. Thank you!
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