activity
20162026
most citedUnsupervised Domain Adaptation of Contextual Embeddings for Low-Resource Duplicate Question Detection

9 citations · 28 across the 23 of their papers we have counts for

collaborators

33 papers

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.AI2025

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Large language models (LLMs) acquire vast knowledge from large text corpora, but this information can become outdated or inaccurate.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,…

cs.CL2025

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…

cs.CL2025

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…