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Research paper

From GPT to Mistral: cross-domain ontology learning with NeOn-GPT

Research paper by Nadeen Fatallah and collaborators, published 2026 in ePrints Soton (University of Southampton). Research area: Semantic Web and Ontologies.

Nadeen Fatallah, Arunav Das, Stefano De Giorgis, Andrea Poltronieri, Peter Haase, Liubov Kovriguina, Elena Paslaru Bontas Simperl, Albert Meroño-Peñuela, Steffen Staab, Alsayed Algergawy
Not specifiedView resource
Research paper

Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts

Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. We investigate the ability of differently-sized LLMs to replicate human writing style in short, creative texts in the domain of Showerthoughts, thoughts that may occur during mundane activities. We compare GPT-2 and GPT-Neo fine-tuned on Reddit data as well as GPT-3.5 invoked in a zero-shot manner, against human-authored texts. We measure human preference on the texts across the specific dimensions that account for the quality of creative, witty texts. Additionally, we compare the ability of humans

Buz, Tolga, Frost, Benjamin, Genchev, Nikola, Schneider, Moritz, Kaffee, Lucie-Aimée, de Melo, Gerard
arXiv.org perpetual, non-exclusive licenseView resource
Research paper

LLMs4Life: Large Language Models for Ontology Learning in Life Sciences

Ontology learning in complex domains, such as life sciences, poses significant challenges for current Large Language Models (LLMs). Existing LLMs struggle to generate ontologies with multiple hierarchical levels, rich interconnections, and comprehensive class coverage due to constraints on the number of tokens they can generate and inadequate domain adaptation. To address these issues, we extend the NeOn-GPT pipeline for ontology learning using LLMs with advanced prompt engineering techniques and ontology reuse to enhance the generated ontologies' domain-specific reasoning and structural depth. Our work evaluates the capabilities of LLMs in o

Fathallah, Nadeen, Staab, Steffen, Algergawy, Alsayed
Creative Commons Attribution 4.0 InternationalView resource
Research paper

MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series

Research paper by Ge Zhang and collaborators. Published 2024-05-29. Topics: Large Language Models, LLMs, GPT, Gemini, Claude.

Ge Zhang, Scott Qu, Jiaheng Liu, Chenchen Zhang, Chenghua Lin, Chou Leuang Yu, Danny Pan, Esther Cheng, Jie Liu, Qunshu Lin, Raven Yuan, Tuney Zheng, Wei Pang, Xinrun Du, Yiming Liang, Yinghao Ma, Yizhi Li, Ziyang Ma, Bill Lin, Emmanouil Benetos, Huan Yang, Junting Zhou, Kaijing Ma, Minghao Liu, Morry Niu, Noah Wang, Quehry Que, Ruibo Liu, Sine Liu, Shawn Guo, Soren Gao, Wangchunshu Zhou, Xinyue Zhang, Yizhi Zhou, Yubo Wang, Yuelin Bai, Yuhan Zhang, Yuxiang Zhang, Zenith Wang, Zhenzhu Yang, Zijian Zhao, Jiajun Zhang, Wanli Ouyang, Wenhao Huang, Wenhu Chen
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