Papers by Yadollah Yaghoobzadeh
Increasing Robustness to Spurious Correlations using Forgettable Examples (2021.eacl-main)
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| Challenge: | Neural NLP models often exploit spurious correlations to perform tasks. minority examples have been shown to increase the out-of-distribution generalization of pre-trained language models. |
| Approach: | They propose to use example forgetting to find minority examples without prior knowledge of spurious correlations in the dataset. |
| Outcome: | The proposed approach improves out-of-distribution generalization on minorities . it shows that minority examples are more robust on challenging datasets . |
PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian (2025.naacl-long)
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Erfan Moosavi Monazzah, Vahid Rahimzadeh, Yadollah Yaghoobzadeh, Azadeh Shakery, Mohammad Taher Pilehvar
| Challenge: | Large language models predominantly reflect Western cultures due to the dominance of English-centric training data. |
| Approach: | They propose a dataset to assess the sensitivity of LLMs to Persian culture. |
| Outcome: | The proposed model shows a 11.3% gap between best closed-source model and layperson baseline while the gap increases to 21.3% by using the best open-weight model. |
Large Language Models for Persian-English Idiom Translation (2025.naacl-long)
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| Challenge: | Large language models have shown superior capabilities in translating figurative language compared to neural machine translation systems. |
| Approach: | They evaluate LLMs, NMTs and their combinations using PersianIdioms datasets . they find that automatic evaluation methods like BLEU and BERTScore are effective . |
| Outcome: | The proposed model performs better in both directions than other models. |
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation (2025.coling-main)
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| Challenge: | Large language models (LLMs) are mainly trained on English data and struggle with low-resource languages. |
| Approach: | They propose to add a new language to Llama to improve classification accuracy for Persian tasks by aligning representations through bilingual pretraining and instruction datasets. |
| Outcome: | The proposed model performs on generation and classification tasks with no adverse impact and sometimes even improvements on English tasks. |
Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference (2022.emnlp-main)
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| Challenge: | Existing methods for debiasing are ineffective in addressing the reverse word-overlap bias. |
| Approach: | They propose to investigate the reverse word-overlap bias in NLI models . they find that existing debiasing methods are generally ineffective . |
| Outcome: | The proposed model is biased towards the non-entailment label on instances with low overlap . the proposed model does not have minority examples, the authors show . |
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing (D18-1)
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| Challenge: | Accurate and complete knowledge bases (KBs) are paramount in NLP. |
| Approach: | They employ multiview learning for increasing the accuracy and coverage of entity type information in KBs by taking high- and low-resource languages from Wikipedia. |
| Outcome: | The proposed learning improves the accuracy and coverage of knowledge bases (KBs) by combining language and representation. |
Comparative Study of Multilingual Idioms and Similes in Large Language Models (2025.coling-main)
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Paria Khoshtab, Danial Namazifard, Mostafa Masoudi, Ali Akhgary, Samin Mahdizadeh Sani, Yadollah Yaghoobzadeh
| Challenge: | figurative language is one of the most challenging aspects of human language for LLMs to comprehend . |
| Approach: | They evaluate LLMs using two multilingual datasets on simile and idiom interpretation and two new evaluation sets for Persian . they find prompt engineering methods are generally effective, but their success varies by figurative type, language, and model. |
| Outcome: | The proposed models perform better in simile and idiom interpretations across languages and figurative types. |
DecompX: Explaining Transformers Decisions by Propagating Token Decomposition (2023.acl-long)
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| Challenge: | Existing vector-based explanation methods for Transformer-based models are limited in their ability to explain the decisions of multiple layers. |
| Approach: | They propose a vector-based explanation method based on the construction of decomposed token representations and their successive propagation throughout the model without mixing them in between layers. |
| Outcome: | The proposed method outperforms existing vector-based and gradient-based methods on transformer-based models by a wide margin. |
ParsiNLU: A Suite of Language Understanding Challenges for Persian (2021.tacl-1)
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Daniel Khashabi, Arman Cohan, Siamak Shakeri, Pedram Hosseini, Pouya Pezeshkpour, Malihe Alikhani, Moin Aminnaseri, Marzieh Bitaab, Faeze Brahman, Sarik Ghazarian, Mozhdeh Gheini, Arman Kabiri, Rabeeh Karimi Mahabagdi, Omid Memarrast, Ahmadreza Mosallanezhad, Erfan Noury, Shahab Raji, Mohammad Sadegh Rasooli, Sepideh Sadeghi, Erfan Sadeqi Azer, Niloofar Safi Samghabadi, Mahsa Shafaei, Saber Sheybani, Ali Tazarv, Yadollah Yaghoobzadeh
| Challenge: | Despite progress in natural language understanding, most progress is concentrated on resource-rich languages like English . despite high-quality benchmarks, there are few available NLU datasets for Persian language . |
| Approach: | They propose a benchmark for Persian language that includes a range of language understanding tasks . they present their results on monolingual and multilingual pre-trained language models . |
| Outcome: | The proposed benchmarks compare human performance with monolingual and multilingual models on Persian language with high quality evaluation datasets. |
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs (C18-1)
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| Challenge: | Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. |
| Approach: | They propose a model that predicts entities at each step of mh-KB paths . the model is based on recurrent neural networks and vector representations of entities and relations . |
| Outcome: | The proposed models show state-of-the-art for two important multi-hop KG reasoning tasks. |
PerCQA: Persian Community Question Answering Dataset (2022.lrec-1)
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| Challenge: | Community Question Answering (CQA) forums provide answers to many real-life questions. |
| Approach: | They propose to make Persian dataset PerCQA public to encourage more research in Persian CQA. |
| Outcome: | The proposed dataset contains 989 questions and 21,915 annotated answers from the most well-known Persian forum. |
Evaluating the Creativity of LLMs in Persian Literary Text Generation (2025.findings-emnlp)
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| Challenge: | Prior research has focused primarily on English, with limited exploration of non-English literary traditions and without standardized methods for assessing creativity. |
| Approach: | They build a dataset of user-generated Persian literary spanning 20 diverse topics and assess model outputs along four creativity dimensions . |
| Outcome: | The proposed models generate Persian literary text enriched with culturally relevant expressions. |
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT (2024.lrec-main)
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Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi, Mohammad Hadi Babalou, Ali Edalat, Sepehr Kamahi, Samin Mahdizadeh Sani, Nikoo Naghavian, Danial Namazifard, Pouya Sadeghi, Yadollah Yaghoobzadeh
| Challenge: | a new study examines the efficacy of large language models (LLMs) for Persian . ChatGPT and LLMs have shown remarkable performance in English, but their efficiency for low-resource languages remains an open question. |
| Approach: | They present a benchmarking study of large language models (LLMs) for Persian . they focus on GPT-3.5-turbo, but also GPT-4 and OpenChat-3.5 . |
| Outcome: | The proposed model performs better in Persian than other low-resource languages . the study is the first comprehensive benchmarking of large language models . |
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction (2025.acl-short)
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| Challenge: | Large language models (LLMs) struggle with zero-shot generalization due to entanglement of general knowledge and task-specific adaptations. |
| Approach: | They propose a modular framework that disentangles general knowledge and adaptations by constructing a library of task-specific LoRA modules alongside a general-domain LoRA. |
| Outcome: | The proposed framework disentangles general knowledge and task-specific adaptations . it generates residual modules that focus more exclusively on task-relevant information . |
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning (2023.acl-short)
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| Challenge: | Recent advances in pre-trained language models have been limited when fine-tuned on small datasets. |
| Approach: | They propose to add contrastive learning to prompt-based fine-tuning to improve model performance. |
| Outcome: | The proposed approach outperforms other methods on multiple text classification benchmarks. |
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings (P19-1)
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| Challenge: | Empirical analysis of word embeddings of ambiguous words is limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. |
| Approach: | They present a large dataset based on manual Wikipedia annotations and word senses, where word sense from different words are related by semantic classes. |
| Outcome: | The proposed method can predict whether a word is single-sense or multi-sensor, if the sense is frequent, and it can predict rare senses. |
Evaluating Cultural Knowledge and Reasoning in LLMs Through Persian Allusions (2025.findings-emnlp)
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| Challenge: | Allusion recognition is a critical test of LLMs' ability to deploy stored information in open-ended, figurative settings. |
| Approach: | They propose a framework for evaluating Persian literary allusions through annotations and LLM-generated texts incorporating allusion in novel contexts. |
| Outcome: | The proposed framework evaluates Persian literary allusions through annotations and LLM-generated texts incorporating allusion in novel contexts. |
GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers (2022.naacl-main)
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| Challenge: | Existing methods for interpreting the underlying dynamics of Transformers have been criticized for their lack of reliability. |
| Approach: | They propose a token attribution analysis method that incorporates all components in the encoder block and aggregates this across layers. |
| Outcome: | The proposed method significantly outperforms existing methods on saliency scores and correlation with gradient-based salience scores. |
Quantifying the Contextualization of Word Representations with Semantic Class Probing (2020.findings-emnlp)
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| Challenge: | Pretrained language models are effective in solving NLP tasks, but there are still questions about how and why they work so well. |
| Approach: | They use BERT to quantify contextualization by studying the extent of inference . they show that top layer representations support highly accurate inference of semantic classes . |
| Outcome: | The proposed model is highly accurate, but weak in the lower layers . it is more task-specific after finetuning while lower layers are more transferable . |
FFE-Hallu: Hallucinations in Fixed Figurative Expressions: A Benchmark of Idioms and Proverbs in the Persian Language (2026.eacl-long)
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| Challenge: | Figurative language, especially fixed figurative expressions, poses unique challenges for large language models . Unlike literal phrases, FFEs are culturally grounded and often non-compositional, making them vulnerable to figurativ hallucination . |
| Approach: | They propose a benchmark to evaluate LLMs' ability to generate, detect, and translate fixed figurative expressions in Persian. |
| Outcome: | The proposed benchmarks show that LLMs still struggle with figurative language expressions . the benchmarks are based on 600 carefully curated examples spanning three tasks . |
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages (2022.acl-long)
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| Challenge: | Existing studies on pre-trained language models assume they encode metaphorical knowledge useful for NLP systems. |
| Approach: | They propose to probing metaphoricity information in PLMs and measure their generalization . they find that contextual representations in PMLs encode metaphorical knowledge . |
| Outcome: | The proposed model can encode metaphorical knowledge across languages and datasets . the model can be used to train and test NLP systems . |
Synthia: Scalable Grounded Persona Generation from Social Media Data (2026.acl-long)
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| Challenge: | Persona-driven large language models (LLMs) are increasingly used in computational social science, yet their validity critically depends on the fidelity of the underlying personas. |
| Approach: | They propose a persona-generation framework that grounds LLM-generated personas in real social-media posts while delegating narrative construction to language models. |
| Outcome: | The proposed framework outperforms state-of-the-art methods for most demographics across different dimensions while maintaining interaction graph structure among personas grounded in real social network users. |
Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers (2023.findings-emnlp)
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| Challenge: | Existing approaches to understanding compositional generalization of models have focused on novel architectures and alternative learning paradigms. |
| Approach: | They propose a method that harnesses the power of dataset cartography to improve model accuracy by strategically identifying a subset of compositional generalization data. |
| Outcome: | The proposed method improves model accuracy by 10% on CFQ and COGS datasets. |