activity
20142025
most citedReducing the Computational Cost in Multi-objective Evolutionary Algorithms by Filtering Worthless Individuals

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Hakim: Farsi Text Embedding Model

Mehran Sarmadi, Morteza Alikhani, Erfan Zinvandi +1

Recent advancements in text embedding have significantly improved natural language understanding across many languages, yet Persian remains notably underrepresented in large-scale…

cs.CL2025

ELAB: Extensive LLM Alignment Benchmark in Persian Language

Zahra Pourbahman, Fatemeh Rajabi, Mohammadhossein Sadeghi +5

This paper presents a comprehensive evaluation framework for aligning Persian Large Language Models (LLMs) with critical ethical dimensions, including safety, fairness, and social…

cs.CL2025

FaMTEB: Massive Text Embedding Benchmark in Persian Language

Erfan Zinvandi, Morteza Alikhani, Mehran Sarmadi +4

In this paper, we introduce a comprehensive benchmark for Persian (Farsi) text embeddings, built upon the Massive Text Embedding Benchmark (MTEB). Our benchmark includes 63 dataset…

cs.CL2024★ 1 cited

OPSD: an Offensive Persian Social media Dataset and its baseline evaluations

Mehran Safayani, Amir Sartipi, Amir Hossein Ahmadi +4

The proliferation of hate speech and offensive comments on social media has become increasingly prevalent due to user activities. Such comments can have detrimental effects on indi…

cs.NE2014★ 2 cited

Reducing the Computational Cost in Multi-objective Evolutionary Algorithms by Filtering Worthless Individuals

Zahra Pourbahman, Ali Hamzeh

The large number of exact fitness function evaluations makes evolutionary algorithms to have computational cost. In some real-world problems, reducing number of these evaluations i…