About the Journal

Artificial Intelligence and Language Models is an open-access, peer-reviewed journal dedicated to advancing research on modern language-centric AI systems, with a particular emphasis on large language models (LLMs) and their real-world deployment. Published by Akira Cipta Solusi, the journal serves as a platform for high-quality contributions from both academic researchers and industry practitioners.

Unlike conventional AI and NLP journals that primarily focus on model development, this journal emphasizes the evaluation, robustness, and practical deployment of language models as real-world systems. It addresses emerging challenges such as benchmarking reliability, adversarial robustness, retrieval-augmented generation (RAG), and responsible AI practices including fairness, transparency, and safety.

The journal emphasizes emerging evaluation paradigms, including LLM-as-a-judge and automated assessment frameworks, as critical components for ensuring the reliability and accountability of modern language models.

The journal also promotes research on multilingual and low-resource language settings, particularly underrepresented languages such as Indonesian and other regional languages, contributing to a more inclusive global AI ecosystem. Through rigorous peer review and support for reproducible research, the journal aims to advance trustworthy, robust, and impactful AI-driven language technologies.

Scope:

• Language Models and LLM Systems
  Design, adaptation, and deployment of modern language models, including transformer-based architectures and instruction-following systems.

• Evaluation and Benchmarking of LLMs
  Reliability, reproducibility, and systematic evaluation of language models, including emerging paradigms such as LLM-as-a-judge, automated evaluation frameworks, meta-evaluation, and alignment between human and model-based assessments.

• Robustness and Adversarial Analysis
  Adversarial attacks, defense mechanisms, safety-critical evaluation, and model trustworthiness.

• Retrieval-Augmented Generation (RAG) and Knowledge Integration
  Hybrid systems combining language models with vector databases, knowledge graphs, and external tools.

• Responsible and Trustworthy AI
  Bias, fairness, hallucination, interpretability, transparency, and ethical deployment of AI systems.

• Multilingual and Low-Resource Language Processing
  Methods and applications for underrepresented languages, including Indonesian and regional contexts.

• Human-AI Interaction and Applied Language Systems
  Real-world applications in healthcare, education, governance, and industry, focusing on usability and impact.

The journal welcomes submissions of original research, empirical evaluations, benchmarking studies, systematic reviews, and real-world applications that advance the science and practice of language model–driven AI. It places particular emphasis on rigorous and reproducible evaluation, including emerging paradigms such as LLM-as-a-judge, as well as the reliable deployment of AI systems in diverse real-world contexts.

ISSN: [to be assigned]

By integrating theoretical advances with empirical validation and real-world impact, Artificial Intelligence and Language Models, published by Akira Cipta Solusi, seeks to provide a dedicated venue for advancing reliable, robust, and inclusive language model research in an increasingly language-driven AI landscape.