Language Acquisition and Understanding

The remarkable capabilities of Large Language Models have pushed the boundaries of artificial intelligence, yet they also highlight a fundamental gap between statistical pattern matching and genuine comprehension. This Special Issue seeks to explore the critical relationship between how an AI system learns language (acquisition) and what it truly understands.

We invite novel research that moves beyond scaling data and parameters to address the core mechanisms of language understanding. We are particularly interested in work that draws inspiration from human cognition, such as data-efficient learning inspired by child development, grounded acquisition that links language to perception and action, and the emergence of compositional reasoning.

Topics of interest include, but are not limited to the following:

- Low-resource and continual language learning;

- Grounded language acquisition in embodied agents;

- Emergent communication and symbolic reasoning;

- The role of interaction and social learning in AI;

- New benchmarks for evaluating deep understanding over surface fluency.

We encourage interdisciplinary submissions that bridge machine learning, cognitive science, and linguistics to help shape the next generation of AI systems that not only generate language but genuinely comprehend it.

Dr. Michal Ptaszynski
Dr. Rafal Rzepka
Prof. Dr. Masaharu Yoshioka
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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 1800 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.

• • Core Concepts: o Language Acquisition o Language Understanding o Meaning Representation o Comprehension vs. Generation o Surface Fluency vs. Deep Understanding • Learning Paradigms and Methods: o Data-Efficient Learning o Low-Resource Language Learning o Continual Learning / Lifelong Learning o Interactive Learning o Self-Supervised Learning o Curriculum Learning • Cognitive and Linguistic Principles: o Compositionality / Systematicity o Generalization o Child Language Development o Cognitive Science o Psycholinguistics o Symbolic Reasoning o Causality in Language • Embodiment and Grounding: o Grounded Language Learning / Language Grounding o Embodied AI / Embodied Agents o Perception and Language o Language and Action o Multimodal Learning • AI Architectures and Models: o Large Language Models (LLMs) o Foundation Models o Neuro-Symbolic AI o Agent-Based Models o Emergent Communication • Evaluation and Analysis: o Evaluation Benchmarks o Probing o Interpretability / Explainability o Robustness o Shortcut Learning