most citedUnveiling Cultural Blind Spots: Analyzing the Limitations of mLLMs in Procedural Text Comprehension

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

cs.CL2026

Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages

Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida +16

Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpreta…

cs.LG2026

AMIR-GRPO: Inducing Implicit Preference Signals into GRPO

Amir Hossein Yari, Fajri Koto

Reinforcement learning has become the primary paradigm for aligning large language models (LLMs) on complex reasoning tasks, with group relative policy optimization (GRPO) widely u…

cs.CL2025

Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages

Amir Hossein Yari, Kalmit Kulkarni, Ahmad Raza Khan +1

While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated almost exclusively for English an…

cs.CL2025★ 1 cited

Unveiling Cultural Blind Spots: Analyzing the Limitations of mLLMs in Procedural Text Comprehension

Amir Hossein Yari, Fajri Koto

Despite the impressive performance of multilingual large language models (mLLMs) in various natural language processing tasks, their ability to understand procedural texts, particu…