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. The journal is published twice a year, in December and June.

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.

Publication Frequency: Twice a year, in in December and June.

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.