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From the 1 of 8 linked papers with an AI index.

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8 papers

cs.DL2026

Scientific Knowledge Discovery in the Age of Large Language Models

Eleni Adamidi, Serafeim Chatzopoulos, Thanasis Vergoulis

The paper surveys 34 peer‑reviewed studies that apply generative large language models to automate scientific literature retrieval and eligibility screening, analyzing model choice…

cs.DL2026

A Template-Driven Platform for Contextualised Researcher Profiles

Serafeim Chatzopoulos, Paris Koloveas, Kleanthis Vichos +2

Modern researchers engage in diverse activities, assume multiple contribution roles, and produce a variety of outputs beyond traditional publications. This broader view of research…

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.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.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…