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

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
2 min read
Context Sensitivity of the huBERT Model in Pragmatic Annotation – New Research Findings
Hungarian developments Featured

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
2 min read
Stanford Innovation in Hypothesis Validation: The POPPER Framework
GitHub Featured

Stanford Innovation in Hypothesis Validation: The POPPER Framework

Researchers at Stanford University unveiled POPPER on 20th February 2025, an automated AI framework that revolutionises hypothesis validation and accelerates scientific discoveries tenfold. Following Karl Popper's principle of falsifiability, POPPER (Automated Hypothesis Validation with Agentic Sequential Falsifications) employs two specialised AI agents: the experiment design agent and the
3 min read