Advances in Generative AI and Computational Linguistics

Dear Colleagues,

The rapid advancement of multi-modal generative AI has profoundly impacted the field of computational linguistics, opening new horizons for integrating diverse data modalities—such as text, images, audio, and video—into cohesive AI-driven systems. The convergence of multi-modal learning and generative models is reshaping how machines understand, process, and generate human language and other forms of communication.

This Special Issue aims to highlight the latest research and developments at the intersection of multi-modal generative AI and computational linguistics. By leveraging multi-modal generative frameworks, researchers are tackling complex linguistic phenomena, enabling systems to perform tasks like cross-modal translation, narrative generation from visual content, and audio-visual dialogue understanding. These innovations are advancing applications in fields such as human-computer interaction, education, entertainment, and assistive technologies.

We invite high-quality original research, reviews, and case studies that explore innovative approaches, frameworks, and real-world applications involving multi-modal generative models in computational linguistics. This Special Issue also welcomes contributions that address challenges such as multi-modal alignment, representation learning, interpretability, and ethical implications.

Topics of interest include, but are not limited to:

• Multi-modal generative AI models for text, image, audio, and video synthesis
• Cross-modal representation learning and fusion techniques
• Visual and audio-grounded language modeling
• Multi-modal dialogue systems and conversational agents
• Text-to-image and image-to-text generation (e.g., DALL·E, CLIP-based methods)
• Multi-modal reasoning and narrative generation
• Computational linguistics approaches to multi-modal data integration
• Ethical concerns, biases, and fairness in multi-modal generative AI
• Applications in education, creative industries, healthcare, and accessibility
• Benchmarking and evaluation methodologies for multi-modal generative systems
• Advances in generative language models and large language models (LLMs)
• Computational linguistics approaches enhanced by generative AI
• Low-resource and multilingual generative NLP
• Dialogue systems and conversational AI
• Text-to-text, text-to-image, and multimodal generative tasks
• Ethical considerations, biases, and explainability in generative AI
• Applications in education, healthcare, legal tech, and social sciences
• Benchmarking and evaluation of generative AI in linguistic contexts

This Special Issue seeks to foster collaboration between the AI, NLP, and computational linguistics communities, promoting state-of-the-art research that pushes the boundaries of how machines understand and generate multi-modal content.

Dr. Yang Zhang
Dr. Haihua Chen
Dr. Kai Wang
Prof. Dr. Kewei Sha
Prof. Dr. Junhua Ding
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• cross-modal learning
• large multi-modal models (LMMs)
• representation learning
• multi-modal reasoning
• explainable and ethical AI
• language understanding and representation
• ethical AI and explainability
• low-resource NLP