Papers by Mete Ismayilzada
DiffuCOMET: Contextual Commonsense Knowledge Diffusion (2024.acl-long)
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| Challenge: | Recent methods for identifying contextually relevant commonsense inferences are weak . knowledge models are trained to verbalize tuples from general commonsens knowledge graphs . |
| Approach: | They develop a series of knowledge models that leverage diffusion to reconstruct semantic connections between narrative contexts and relevant commonsense knowledge. |
| Outcome: | The proposed model improves on two benchmarks, ComFact and WebNLG+, to measure commonsense diversity and contextual relevance. |
Exploring Defeasibility in Causal Reasoning (2024.findings-acl)
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Shaobo Cui, Lazar Milikic, Yiyang Feng, Mete Ismayilzada, Debjit Paul, Antoine Bosselut, Boi Faltings
| Challenge: | Existing studies ignore defeasibility in causal reasoning and fail to evaluate existing causal strength metrics in defensible settings. |
| Approach: | They propose a metric that measures causal strength based on token-level causal relationships. |
| Outcome: | The proposed metric improves on existing metrics by 69.7% . supporters and defeaters are more effective than opponents, the authors show . |
CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks (2023.emnlp-main)
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| Challenge: | Recent efforts in natural language processing (NLP) commonsense reasoning research have produced a number of new datasets and benchmarks. |
| Approach: | They propose a manually-curated, multi-task benchmark that evaluates models' ability to apply commonsense reasoning in the context of six real-world NLP tasks. |
| Outcome: | The proposed benchmark evaluates the ability of models to apply commonsense reasoning in the context of six real-world NLP tasks. |
Creative Preference Optimization (2025.findings-emnlp)
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Mete Ismayilzada, Antonio Laverghetta Jr., Simone A. Luchini, Reet Patel, Antoine Bosselut, Lonneke Van Der Plas, Roger E. Beaty
| Challenge: | Existing methods for enhancing LLM creativity focus on diversity or specific tasks, failing to address creativity’s multifaceted nature in a generalizable way. |
| Approach: | They propose a method that injects signals from multiple creativity dimensions into the preference optimization objective in a modular fashion. |
| Outcome: | The proposed method outperforms baseline models on automated and human evaluations while maintaining high output quality. |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
| Challenge: | Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |
kogito: A Commonsense Knowledge Inference Toolkit (2023.eacl-demo)
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| Challenge: | kogito provides an intuitive and extensible interface to interact with natural language generation models. |
| Approach: | They propose to use kogito to generate commonsense inferences from text . they use a standardized API for training and evaluating knowledge models . |
| Outcome: | The proposed tool provides an intuitive and extensible interface to interact with natural language generation models. |
REFINER: Reasoning Feedback on Intermediate Representations (2024.eacl-long)
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Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, Boi Faltings
| Challenge: | Language models (LLMs) have shown remarkable performance by explicitly generating intermediate inferences,e.g., chain-of-thought prompting. |
| Approach: | They propose a framework for finetuning LMs to generate intermediate reasoning steps while interacting with a critic model that provides automated feedback on the reasoning. |
| Outcome: | Empirical evaluations of REFINER on three diverse reasoning tasks show that it significantly improves over baseline models. |
Evaluating Morphological Compositional Generalization in Large Language Models (2025.naacl-long)
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Mete Ismayilzada, Defne Circi, Jonne Sälevä, Hale Sirin, Abdullatif Köksal, Bhuwan Dhingra, Antoine Bosselut, Duygu Ataman, Lonneke Van Der Plas
| Challenge: | Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. |
| Approach: | They define morphemes as compositional primitives and design a suite of generative and discriminative tasks to assess morphological productivity and systematicity. |
| Outcome: | The proposed models can identify individual morphological combinations better than chance, but their performance lacks systematicity, leading to significant accuracy gaps compared to humans. |