Symmetry and Asymmetry in Large Language Models-Based Natural Language Processing
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 135
Special Issue Editor
Special Issue Information
Dear Colleagues,
The field of Natural Language Processing (NLP) has witnessed remarkable advancements, particularly with the rise of large language models (LLMs) like ChatGPT and DeepSeek. However, the concepts of symmetry and asymmetry remain underexplored, despite their profound implications. Symmetry in LLMs can manifest in various forms, such as symmetric attention mechanisms, balanced dataset distributions, and harmonious linguistic structures, which contribute to model robustness and interpretability. For instance, symmetric relations in syntax and semantics can enhance the model's ability to generalize across different language constructs.
Conversely, asymmetry in LLMs, evident in imbalanced training data, unequal attention weights, and divergent language patterns, poses significant challenges and opportunities. Asymmetric phenomena can lead to biases, reduced performance on minority classes, and difficulties in handling long-range dependencies. Addressing these asymmetries is crucial for developing fairer, more accurate, and contextually aware NLP systems.
Understanding and leveraging these symmetries and asymmetries can lead to more robust, interpretable, and efficient LLMs. This Special Issue aims to delve into these aspects, exploring how symmetric relations and asymmetric phenomena influence LLM architectures, training methodologies, and real-world applications. We welcome contributions that investigate these themes through original research, case studies, and comprehensive reviews.
Dr. Jian Liao
Guest Editor
Manuscript Submission Information
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Keywords
- large language models
- natural language processing
- dataset imbalance
- linguistic structures
- model robustness
- interpretability
- bias mitigation
- long-range dependencies
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