What is this collection about?
Large language models (LLMs) are reshaping how information is generated, analysed, and communicated across domains including science, healthcare, education, and industry. Their rapid adoption has highlighted the need to ensure that these systems are trustworthy, safe, robust, and secure, particularly as they are increasingly used in high-impact and decision-critical settings.
This Collection welcomes research that addresses challenges such as:
- Trustworthiness and reliability of large language models
- Safety and truthfulness in LLM outputs
- Robustness to distribution shifts and adversarial attacks
- Evaluation and benchmarking of LLM performance and reliability
- Bias detection, fairness, and responsible AI practices
- Explainability and interpretability of LLMs
Why is this collection important?
By bringing together advances in evaluation, benchmarking, interpretability, fairness, and robustness, this Collection aims to support the development of more reliable and trustworthy AI systems and facilitate their responsible deployment in real-world applications.
Why submit to a collection?
Collections like this one help promote high-quality science. They are led by In-House Editors who are experts in their fields and supported by a dedicated team of Commissioning Editors and Managing Editors at Springer Nature. Collection manuscripts typically see higher citations, downloads, and Altmetric scores, and provide a one-stop-shop on a cutting-edge topic of interest.
Who is involved?
Communications AI & Computing is edited by in-house professional editors and Editorial Board Members.
How can I submit my paper?
Visit the Collection page to find out more about this collection and submit your article.