36 citations · 74 across the 12 of their papers we have counts for
13 papers
Large Language Models Align with the Human Brain during Creative Thinking
Mete Ismayilzada, Simone A. Luchini, Abdulkadir Gokce +5
Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engin…
CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge
Mete Ismayilzada, Renqing Cuomao, Daniil Yurshevich +3
Creative problem-solving requires combining multiple cognitive abilities, including logical reasoning, lateral thinking, analogy-making, and commonsense knowledge, to discover insi…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
Creative Preference Optimization
Mete Ismayilzada, Antonio Laverghetta, Simone A. Luchini +4
While Large Language Models (LLMs) have demonstrated impressive performance across natural language generation tasks, their ability to generate truly creative content-characterized…
Evaluating Creative Short Story Generation in Humans and Large Language Models
Mete Ismayilzada, Claire Stevenson, Lonneke van der Plas
Story-writing is a fundamental aspect of human imagination, relying heavily on creativity to produce narratives that are novel, effective, and surprising. While large language mode…
Creativity in AI: Progresses and Challenges
Mete Ismayilzada, Debjit Paul, Antoine Bosselut +1
Creativity is the ability to produce novel, useful, and surprising ideas, and has been widely studied as a crucial aspect of human cognition. Machine creativity on the other hand h…
Evaluating Morphological Compositional Generalization in Large Language Models
Mete Ismayilzada, Defne Circi, Jonne Sälevä +6
Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. However, their linguistic generalization capabil…
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
Beatriz Borges, Negar Foroutan, Deniz Bayazit +87
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they a…
DiffuCOMET: Contextual Commonsense Knowledge Diffusion
Silin Gao, Mete Ismayilzada, Mengjie Zhao +3
Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models. In this work, we develop a series of knowledge models, Di…
Exploring Defeasibility in Causal Reasoning
Shaobo Cui, Lazar Milikic, Yiyang Feng +4
Defeasibility in causal reasoning implies that the causal relationship between cause and effect can be strengthened or weakened. Namely, the causal strength between cause and effec…
CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks
Mete Ismayilzada, Debjit Paul, Syrielle Montariol +2
Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these dataset…
REFINER: Reasoning Feedback on Intermediate Representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard +4
Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, the…
kogito: A Commonsense Knowledge Inference Toolkit
Mete Ismayilzada, Antoine Bosselut
In this paper, we present kogito, an open-source tool for generating commonsense inferences about situations described in text. kogito provides an intuitive and extensible interfac…