Zobrazeno 1 - 10
of 507
pro vyhledávání: '"Auer, Sören"'
The growing volume of biomedical scholarly document abstracts presents an increasing challenge in efficiently retrieving accurate and relevant information. To address this, we introduce a novel approach that integrates an optimized topic modelling fr
Externí odkaz:
http://arxiv.org/abs/2411.00041
In response to the growing complexity and volume of scientific literature, this paper introduces the LLMs4Synthesis framework, designed to enhance the capabilities of Large Language Models (LLMs) in generating high-quality scientific syntheses. This
Externí odkaz:
http://arxiv.org/abs/2409.18812
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 the 23rd International Semantic Web Conference (ISWC) to explore the p
Externí odkaz:
http://arxiv.org/abs/2409.10146
The increasing amount of published scholarly articles, exceeding 2.5 million yearly, raises the challenge for researchers in following scientific progress. Integrating the contributions from scholarly articles into a novel type of cognitive knowledge
Externí odkaz:
http://arxiv.org/abs/2409.06433
Publikováno v:
Linking Theory and Practice of Digital Libraries. TPDL 2024. Lecture Notes in Computer Science, vol 15177
Authoring survey or review articles still requires significant tedious manual effort, despite many advancements in research knowledge management having the potential to improve efficiency, reproducibility, and reuse. However, these advancements bring
Externí odkaz:
http://arxiv.org/abs/2407.18657
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 syntheses, comparing their evaluations to those of human annotators.
Externí odkaz:
http://arxiv.org/abs/2407.02977
Autor:
Giglou, Hamed Babaei, Taffa, Tilahun Abedissa, Abdullah, Rana, Usmanova, Aida, Usbeck, Ricardo, D'Souza, Jennifer, Auer, Sören
This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a unified and intuitive
Externí odkaz:
http://arxiv.org/abs/2406.07257
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 defined as the extraction of (Task, Dataset, Metric, Score) quadruples from
Externí odkaz:
http://arxiv.org/abs/2407.02409
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-Mistral 7B, Llama-2, GPT-4-Turbo and GPT-4.o in extracting leaderboard
Externí odkaz:
http://arxiv.org/abs/2406.04383
Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM systems often rely on expert knowledge or predictive models, with lim
Externí odkaz:
http://arxiv.org/abs/2404.10317