3 papers
math.ST2026
Two Sample Sizes in Test-Time LLM Ensembling: Buffered ERM and Minimax Theory
Zhi Zhang, Arash A. Amini
Test-time scaling can improve the performance of large language models by generating multiple answers to a question and aggregating them. Weighted ensembles extend this idea by dra…
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…