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Yadollah Yaghoobzadeh

14 accepted papers

2025

Comparative Study of Multilingual Idioms and Similes in Large Language Models

Paria Khoshtab, Danial Namazifard, Mostafa Masoudi, Ali Akhgary, Samin Mahdizadeh Sani, Yadollah Yaghoobzadeh
COLING 2025main

This study addresses the gap in the literature concerning the comparative performance of LLMs in interpreting different types of figurative language across multiple languages. By evaluating LLMs using two multilingual datasets on simile and idiom interpretation, we explore the effectiveness of vario…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2025

Evaluating Cultural Knowledge and Reasoning in LLMs Through Persian Allusions

Melika Nobakhtian, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar
EMNLP 2025

Allusion recognition—a task demanding contextual activation of cultural knowledge—serves as a critical test of LLMs’ ability to deploy stored information in open-ended, figurative settings. We introduce a framework for evaluating Persian literary allusions through (1) classical poetry annotations an

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Evaluating the Creativity of LLMs in Persian Literary Text Generation

Armin Tourajmehr, Mohammad Reza Modarres, Yadollah Yaghoobzadeh
EMNLP 2025

Large language models (LLMs) have demonstrated notable creative abilities in generating literary texts, including poetry and short stories. However, prior research has primarily centered on English, with limited exploration of non-English literary traditions and without standardized methods for asse

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2025

Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation

Samin Mahdizadeh Sani, Pouya Sadeghi, Thuy-Trang Vu, Yadollah Yaghoobzadeh, Gholamreza Haffari
COLING 2025main

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited underst…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction

Mohammadtaha Bagherifard, Sahar Rajabi, Ali Edalat, Yadollah Yaghoobzadeh
ACL 2025short

Large language models (LLMs) often struggle with zero-shot generalization, and several modular approaches have been proposed to address this challenge. Yet, we hypothesize that a key limitation remains: the entanglement of general knowledge and task-specific adaptations. To overcome this, we propose…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Large Language Models for Persian-English Idiom Translation

Sara Rezaeimanesh, Faezeh Hosseini, Yadollah Yaghoobzadeh
NAACL 2025long

Large language models (LLMs) have shown superior capabilities in translating figurative language compared to neural machine translation (NMT) systems. However, the impact of different prompting methods and LLM-NMT combinations on idiom translation has yet to be thoroughly investigated. This paper in…

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2025

PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian

Erfan Moosavi Monazzah, Vahid Rahimzadeh, Yadollah Yaghoobzadeh, Azadeh Shakery, Mohammad Taher Pilehvar
NAACL 2025long

Large language models predominantly reflect Western cultures, largely due to the dominance of English-centric training data. This imbalance presents a significant challenge, as LLMs are increasingly used across diverse contexts without adequate evaluation of their cultural competence in non-English…

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2024

Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT

Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi, Mohammad Hadi Babalou, Ali Edalat +6
COLING 2024main

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs acros…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗
2023

DecompX: Explaining Transformers Decisions by Propagating Token Decomposition

Ali Modarressi, Mohsen Fayyaz, Ehsan Aghazadeh, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar
ACL 2023long

An emerging solution for explaining Transformer-based models is to use vector-based analysis on how the representations are formed. However, providing a faithful vector-based explanation for a multi-layer model could be challenging in three aspects: (1) Incorporating all components into the analysis…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2023

Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers

Osman Batur İnce, Tanin Zeraati, Semih Yagcioglu, Yadollah Yaghoobzadeh, Erkut Erdem, Aykut Erdem
EMNLP 2023long findings

Neural networks have revolutionized language modeling and excelled in various downstream tasks. However, the extent to which these models achieve compositional generalization comparable to human cognitive abilities remains a topic of debate. While existing approaches in the field have mainly focused…

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2023

LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning

Amirhossein Abaskohi, Sascha Rothe, Yadollah Yaghoobzadeh
ACL 2023short

In recent years, there has been significant progress in developing pre-trained language models for NLP. However, these models often struggle when fine-tuned on small datasets. To address this issue, researchers have proposed various adaptation approaches. Prompt-based tuning is arguably the most com…

Cited by 26Source ↗PDF ↗code ↗Scholar ↗
2022

GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers

Ali Modarressi, Mohsen Fayyaz, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar
NAACL 2022long

There has been a growing interest in interpreting the underlying dynamics of Transformers. While self-attention patterns were initially deemed as the primary option, recent studies have shown that integrating other components can yield more accurate explanations. This paper introduces a novel token…

Cited by 38Source ↗PDF ↗code ↗Scholar ↗
2022

Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference

Sara Rajaee, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar
EMNLP 2022main

It has been shown that NLI models are usually biased with respect to the word-overlap between the premise and the hypothesis, as they take this feature as a primary cue for predicting the entailment label. In this paper, we focus on an overlooked aspect of the overlap bias in the NLI models: the rev…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2022

Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages

Ehsan Aghazadeh, Mohsen Fayyaz, Yadollah Yaghoobzadeh
ACL 2022long

Human languages are full of metaphorical expressions. Metaphors help people understand the world by connecting new concepts and domains to more familiar ones. Large pre-trained language models (PLMs) are therefore assumed to encode metaphorical knowledge useful for NLP systems. In this paper, we inv…

Cited by 65Source ↗PDF ↗code ↗Scholar ↗