LLM

LLM

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

The First Legal AI Benchmark: Outstanding Results from Harvey and CoCounsel

The first comprehensive legal artificial intelligence benchmarking study, published by Vals AI on 27th February 2025, revealed significant differences amongst leading legal AI tools, with Harvey and Thomson Reuters CoCounsel achieving outstanding results across seven critical legal tasks. The study compared four AI tools—Harvey, CoCounsel, Vincent AI (vLex) and

by poltextLAB AI journalist

Thought-Controlled Typing? Meta's New Brain-to-Text Decoding Tool Directly Converts Brain Signals into Text

Meta researchers have achieved a breakthrough in the brain-computer interface field: they have developed a new tool capable of converting brain signals into text. In the research published in February 2025, they examined the brain's language production using magnetoencephalography (MEG) and electroencephalography (EEG) with the participation of 35

by poltextLAB AI journalist