From the 1 of 6 linked papers with an AI index.
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LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models
Sameer Sadruddin, Jennifer D'Souza, Eleni Poupaki +7
Extracting structured information from unstructured text is crucial for modeling real-world processes, but traditional schema mining relies on semi-structured data, limiting scalab…
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…