Publications by Mahammad Ismayilzada

Related publications (5)

Exploring Defeasibility in Causal Reasoning

Boi Faltings, Antoine Bosselut, Debjit Paul, Shaobo Cui, Mahammad Ismayilzada, Lazar Milikic

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 effect should increase or decrease with the incorporation of strengthening argume ...

2024

CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks

Antoine Bosselut, Syrielle Mathilde Marie Montariol, Debjit Paul, Mahammad Ismayilzada

Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these datasets formulate commonsense reasoning challenges in artificial scenarios that ar ...

Association for Computational Linguistics (ACL)2023

REFINER: Reasoning Feedback on Intermediate Representations

Boi Faltings, Robert West, Antoine Bosselut, Maxime Jean Julien Peyrard, Debjit Paul, Mahammad Ismayilzada, Beatriz Maria Borges Ribeiro

Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, these intermediate inference steps may be in- appropriate deductions from the i ...

2023

kogito: A Commonsense Knowledge Inference Toolkit

Antoine Bosselut, Mahammad Ismayilzada

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 interface to interact with natural language generation models that can be used for h ...

Assoc Computational Linguistics-Acl2023

Evaluating Morphological Compositional Generalization in Large Language Models

Antoine Bosselut, Mahammad Ismayilzada

Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. However, their linguistic generalization capabilities remain questionable, raising doubts about whether these models learn l ...

Association for Computational Linguistics2025