Applications of Natural Language to Data Bases

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Recent advances in AI have increased the expectations for users when it comes to information access systems. With powerful LLMs, users engage with information using natural language instead of artificial query languages. At the same time, this raises not only technical but also ethical concerns, such as sustainability, reliability, and privacy. NLDB has established itself as a venue to discuss precisely the intersection of natural language and information systems. We invite researchers and practitioners to contribute. Topics of Interest include (but are not limited to): Multimodality, AI safety and ethics, Interactivity and Natural Language Interfaces, Social Media and Web Data, eXplainable AI, Interpretability and Model Analysis in NLP, Generative models, Large Language Models, Information Retrieval and Text Mining, Discourse and Pragmatics, Sentiment Analysis, Argument Mining, Question Answering, Dialogue, and Interactive Systems, NLP Applications, Efficient/Low-resource methods in NLP, Big Data and Scalability. Conference tracks: Main - The main track solicits novel and significant research contributions addressing theoretical aspects, algorithms, applications, architectures, resources, and other aspects of NLP, as well as survey and discussion papers. We welcome work describing original and replicable research showing evidence of significant contribution to the NLP community. Industry - The industry track covers all aspects of innovative commercial or industrial-strength NLP technologies in order to showcase the state of adoption. It welcomes contributions about case studies of success stories, discussion reports of obstacles that stand in the way of adoption of NLP technologies, and experience reports in applying recent research advances to relevant industry problems. We encourage results and ideas from companies small and large. Publication: The conference proceedings will be published in Lecture Notes in Computer Science (LNCS, Springer).

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