collaborators

6 papers

cs.AI2025

InsightGUIDE: An Opinionated AI Assistant for Guided Critical Reading of Scientific Literature

Paris Koloveas, Serafeim Chatzopoulos, Thanasis Vergoulis +1

The proliferation of scientific literature presents an increasingly significant challenge for researchers. While Large Language Models (LLMs) offer promise, existing tools often pr…

cs.DL2025

Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers

Paris Koloveas, Serafeim Chatzopoulos, Dionysis Diamantis +2

The growing volume of scientific literature makes it challenging for scientists to move from a list of papers to a synthesized understanding of a topic. Because of the constant inf…

cs.DL2025

From raw affiliations to organization identifiers

Myrto Kallipoliti, Serafeim Chatzopoulos, Miriam Baglioni +3

Accurate affiliation matching, which links affiliation strings to standardized organization identifiers, is critical for improving research metadata quality, facilitating comprehen…

cs.AI2025

Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)

Gavin Farrell, Eleni Adamidi, Rafael Andrade Buono +27

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an…

cs.DC2025

A Virtual Laboratory for Managing Computational Experiments

Eleni Adamidi, Panayiotis Deligiannis, Nikos Foutris +1

Computational experiments have become essential for scientific discovery, allowing researchers to test hypotheses, analyze complex datasets, and validate findings. However, as comp…

cs.CL2025

Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs

Paris Koloveas, Serafeim Chatzopoulos, Thanasis Vergoulis +1

This work investigates the ability of open Large Language Models (LLMs) to predict citation intent through in-context learning and fine-tuning. Unlike traditional approaches relyin…