3 citations · 12 across the 15 of their papers we have counts for
6 papers · 2 filters
LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesis
Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer
In response to the growing complexity and volume of scientific literature, this paper introduces the LLMs4Synthesis framework, designed to enhance the capabilities of Large Languag…
LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge
Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer
This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with…
Exploring the Latest LLMs for Leaderboard Extraction
Salomon Kabongo, Jennifer D'Souza, Sören Auer
The rapid advancements in Large Language Models (LLMs) have opened new avenues for automating complex tasks in AI research. This paper investigates the efficacy of different LLMs-M…
Large Language Models as Evaluators for Scientific Synthesis
Julia Evans, Jennifer D'Souza, Sören Auer
Our study explores how well the state-of-the-art Large Language Models (LLMs), like GPT-4 and Mistral, can assess the quality of scientific summaries or, more fittingly, scientific…
Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study
Salomon Kabongo, Jennifer D'Souza, Sören Auer
This paper explores the impact of context selection on the efficiency of Large Language Models (LLMs) in generating Artificial Intelligence (AI) research leaderboards, a task defin…
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology
Mahsa Shamsabadi, Jennifer D'Souza, Sören Auer
In this paper, we champion the use of structured and semantic content representation of discourse-based scholarly communication, inspired by tools like Wikipedia infoboxes or struc…