565 citations · 631 across the 33 of their papers we have counts for
38 papers
Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation
Zahra Habibzadeh, Paria Khoshtab, Amir Mesbah +1
Transforming a dense, abstract proverb into an engaging and morally faithful narrative requires deep cultural understanding and robust semantic grounding. We frame this problem as…
GhazalBench: Canonical Verse Access in LLMs across Persian Ghazals and Shakespearean Sonnets
Ghazal Kalhor, Yadollah Yaghoobzadeh
Persian poetry plays an active role in Iranian cultural practice, where verses by canonical poets such as Hafez and Saadi are frequently quoted, paraphrased, or completed from inco…
Layer-wise Positional Bias in Short-Context Language Modeling
Maryam Rahimi, Mahdi Nouri, Yadollah Yaghoobzadeh
Transformer language models systematically prefer tokens at specific input positions regardless of semantic relevance---a phenomenon known as positional bias. Prior work characteri…
A Dual-Axis Taxonomy of Knowledge Editing for LLMs: From Mechanisms to Functions
Amir Mohammad Salehoof, Ali Ramezani, Yadollah Yaghoobzadeh +1
Large language models (LLMs) acquire vast knowledge from large text corpora, but this information can become outdated or inaccurate. Since retraining is computationally expensive,…
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction
Mohammadtaha Bagherifard, Sahar Rajabi, Ali Edalat +1
Large language models often struggle with zero-shot generalization, and several modular approaches have been proposed to address this challenge. Yet, we hypothesize that a key limi…
PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian
Erfan Moosavi Monazzah, Vahid Rahimzadeh, Yadollah Yaghoobzadeh +2
Large language models predominantly reflect Western cultures, largely due to the dominance of English-centric training data. This imbalance presents a significant challenge, as LLM…
Explanations of Large Language Models Explain Language Representations in the Brain
Maryam Rahimi, Mohammad Reza Daliri, Yadollah Yaghoobzadeh
Large Language Model (LLM) representations are known to align with brain activity during language processing, but it remains unclear what drives this alignment. We test whether exp…
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation
Samin Mahdizadeh Sani, Pouya Sadeghi, Thuy-Trang Vu +2
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-reso…
Large Language Models for Persian English Idiom Translation
Sara Rezaeimanesh, Faezeh Hosseini, Yadollah Yaghoobzadeh
Large language models (LLMs) have shown superior capabilities in translating figurative language compared to neural machine translation (NMT) systems. However, the impact of differ…
Comparative Study of Multilingual Idioms and Similes in Large Language Models
Paria Khoshtab, Danial Namazifard, Mostafa Masoudi +3
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 e…
Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models
Sepehr Kamahi, Yadollah Yaghoobzadeh
Despite the widespread adoption of autoregressive language models, explainability evaluation research has predominantly focused on span infilling and masked language models. Evalua…
uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?
Pouya Sadeghi, Amirhossein Abaskohi, Yadollah Yaghoobzadeh
Inspired by human cognition, Jiang et al.(2023c) create a benchmark for assessing LLMs' lateral thinking-thinking outside the box. Building upon this benchmark, we investigate how…
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT
Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi +9
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 m…
Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers
Osman Batur İnce, Tanin Zeraati, Semih Yagcioglu +3
Neural networks have revolutionized language modeling and excelled in various downstream tasks. However, the extent to which these models achieve compositional generalization compa…
DecompX: Explaining Transformers Decisions by Propagating Token Decomposition
Ali Modarressi, Mohsen Fayyaz, Ehsan Aghazadeh +2
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 expla…
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
Amirhossein Abaskohi, Sascha Rothe, Yadollah Yaghoobzadeh
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 a…
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation
Alireza Salemi, Amirhossein Abaskohi, Sara Tavakoli +2
Multilingual pre-training significantly improves many multilingual NLP tasks, including machine translation. Most existing methods are based on some variants of masked language mod…
BERT on a Data Diet: Finding Important Examples by Gradient-Based Pruning
Mohsen Fayyaz, Ehsan Aghazadeh, Ali Modarressi +3
Current pre-trained language models rely on large datasets for achieving state-of-the-art performance. However, past research has shown that not all examples in a dataset are equal…
Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference
Sara Rajaee, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar
It has been shown that NLI models are usually biased with respect to the word-overlap between premise and hypothesis; they take this feature as a primary cue for predicting the ent…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers
Ali Modarressi, Mohsen Fayyaz, Yadollah Yaghoobzadeh +1
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…
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages
Ehsan Aghazadeh, Mohsen Fayyaz, Yadollah Yaghoobzadeh
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 lan…
PerCQA: Persian Community Question Answering Dataset
Naghme Jamali, Yadollah Yaghoobzadeh, Hesham Faili
Community Question Answering (CQA) forums provide answers for many real-life questions. Thanks to the large size, these forums are very popular among machine learning researchers.…
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Daniel Khashabi, Arman Cohan, Siamak Shakeri +22
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich l…