DadmaTools V2 is a comprehensive repository designed to enhance NLP capabilities for the Persian language, catering to industry practitioners seeking practical and efficient solutions. The toolkit provides extensive code examples demonstrating the integration of its models with popular NLP frameworks such as Trankit and Transformers, as well as deep learning frameworks like PyTorch. Additionally, DadmaTools supports widely used Persian embeddings and datasets, ensuring robust language processing capabilities. The latest version of DadmaTools introduces an adapter-based technique, significantly reducing memory usage by employing a shared pre-trained model across various tasks, supplemented with task-specific adapter layers. This approach eliminates the need to maintain multiple pre-trained models and optimize resource utilization. Enhancements in this version include adding new modules such as a sentiment detector, an informal-to-formal text converter, and a spell checker, further expanding the toolkit’s functionality. DadmaTools V2 thus represents a powerful, efficient, and versatile resource for advancing Persian NLP applications.

This study introduces a novel framework for evaluating Large Language Models (LLMs) and Vision-Language Models (VLMs) in Persian, a low-resource language. We develop comprehensive datasets to assess reasoning, linguistic understanding, and multimodal capabilities. Our datasets include Persian-OCR-QA for optical character recognition, Persian-VQA for visual question answering, Persian world-image puzzle for multimodal integration, Visual-Abstraction-Reasoning for abstract reasoning, and Iran-places for visual knowledge of Iranian figures and locations. We evaluate models like GPT-4o, Claude 3.5 Sonnet, and Llama 3.2 90B Vision, revealing their strengths and weaknesses in processing Persian. This research contributes to inclusive language processing by addressing the unique challenges of low-resource language evaluation.

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work.