Costs of Generative AI Applications: Hardware Costs and Resource Requirements from the Issuer's Perspective

The emergence of large language models (LLMs) and generative AI applications has ushered in a new era of artificial intelligence capabilities, fundamentally altering the landscape of computational requirements and associated costs. Generative AI systems, built upon transformer architectures and trained on vast datasets, have demonstrated remarkable scalability and adaptability across

Corpus Size vs Quality: New Research on the Efficiency of Hungarian Language Models

Hungarian language technology research has reached a significant milestone: a comprehensive study has revealed that a larger corpus size does not necessarily lead to improved performance in morphological analysis. In their study, Andrea Dömötör, Balázs Indig, and Dávid Márk Nemeskey conducted a detailed analysis of three Hungarian-language corpora of varying

by poltextLAB AI journalist

Context Sensitivity of the huBERT Model in Pragmatic Annotation – New Research Findings

Tibor Szécsényi and Nándor Virág, researchers at the University of Szeged, have explored the context sensitivity of the huBERT language model in pragmatic annotation, focusing in particular on the automatic identification of imperative verb functions. Their study, conducted on the MedCollect corpus—a dataset of health-related misinformation—investigates how both

by poltextLAB AI journalist

Retrieval-Augmented Generation (RAG): Architecture, Mechanisms, and Core Advantages

Retrieval-Augmented Generation (RAG) represents a paradigm shift in natural language processing (NLP), integrating large language models (LLMs) with dynamic information retrieval systems to produce responses that are both contextually enriched and factually grounded (Lewis et al. 2020). At its core, the RAG architecture couples a conventional generative model—one that

China’s Response to OpenAI’s Sora Model: StepFun Unveils a 30-Billion-Parameter AI System

On 17 February 2025, Chinese company StepFun publicly released its open-source text-to-video generation model, Step-Video-T2V, featuring 30 billion parameters. Positioned as a direct competitor to OpenAI’s Sora, the model interprets bilingual (English and Chinese) text prompts and can generate videos of up to 204 frames in 544×992 resolution.

by poltextLAB AI journalist