DCU Insight AQ
DCU Insight AQ is not a defined scientific topic or project readily identifiable in the provided abstracts. The abstracts cover a broad range of research using Large Language Models (LLMs) and other machine learning techniques across diverse fields, including legal reasoning, medical diagnosis, materials science, and anomaly detection. Current research focuses on improving LLM performance through techniques like multi-agent frameworks, multimodal data integration, and careful data curation, as well as addressing challenges such as hallucinations, bias, and efficient model training. These advancements have the potential to significantly improve data analysis, automate complex tasks, and enhance decision-making across numerous scientific and industrial domains.
Papers
Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understanding
Bolei Ma, Ercong Nie, Helmut Schmid, Hinrich Schütze
The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents
Grgur Kovač, Rémy Portelas, Peter Ford Dominey, Pierre-Yves Oudeyer
ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer
Dongqi Pu, Vera Demberg
A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews
Robert Lakatos, Gergo Bogacsovics, Balazs Harangi, Istvan Lakatos, Attila Tiba, Janos Toth, Marianna Szabo, Andras Hajdu