LLM

LLM

DeepSeek's New Development Targets General and Highly Scalable AI Reward Models

On 8 April 2025, Chinese DeepSeek AI introduced its novel technology, Self-Principled Critique Tuning (SPCT), marking a significant advancement in the reward mechanisms of large language models. SPCT is designed to enhance AI models’ performance in handling open-ended, complex tasks, particularly in scenarios requiring nuanced interpretation of context and user

by poltextLAB AI journalist

Google Has Introduced a New Model Family: Gemini 2.5, the Company’s Most Advanced Reasoning Model to Date

Google unveiled the Gemini 2.5 artificial intelligence model family on March 25, 2025, representing the company’s most advanced reasoning AI system to date. The first released version, Gemini 2.5 Pro Experimental, is capable of reasoning before responding, significantly improving performance and accuracy. The model is already available

by poltextLAB AI journalist

Tencent Has Unveiled a New Model: 44% Faster Response Time and Double the Word Generation Speed

On 27 February 2025, Chinese tech giant Tencent unveiled its latest “fast-thinking” artificial intelligence model, the Hunyuan Turbo S. Compared to the DeepSeek R1 model, it boasts a 44% reduction in response time and twice the word generation speed. The new model adopts an innovative Hybrid-Mamba-Transformer architecture, which significantly reduces

by poltextLAB AI journalist

Microsoft Phi-4: Compact model with multimodal capabilities

In February 2025, Microsoft introduced two new members of the Phi-4 model family, with the Phi-4-multimodal-instruct being particularly noteworthy. Despite having just 5.6 billion parameters, it can simultaneously process text, images, and audio, while its performance in certain tasks remains competitive with models twice its size. The Phi-4-multimodal-instruct was

by poltextLAB AI journalist

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