LLM

A collection of 36 posts
OpenAI PaperBench Measures AI Agents' Performance in Reconstructing Scientific Papers
OpenAI Featured

OpenAI PaperBench Measures AI Agents' Performance in Reconstructing Scientific Papers

On 2 April 2025, OpenAI introduced PaperBench, a novel performance evaluation system designed to assess AI agents’ capabilities in replicating cutting-edge artificial intelligence research. Developed as part of the OpenAI Preparedness Framework, which measures AI systems’ readiness for complex tasks, PaperBench specifically challenges AI agents to accurately replicate 20 significant
3 min read
Foundation Agents: Data-Driven Enterprise Efficiency in 2025
agents Featured

Foundation Agents: Data-Driven Enterprise Efficiency in 2025

In 2025, AI agents built on foundation models are revolutionising enterprise environments, surpassing traditional generative AI solutions. While most organisations still deploy ChatGPT-like applications, leading companies are adopting autonomous AI agents that respond to commands and execute complex business processes with minimal human intervention. Data-driven results from enterprise implementations demonstrate
3 min read
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