Papers by Amir Hossein Yari
Unveiling Cultural Blind Spots: Analyzing the Limitations of mLLMs in Procedural Text Comprehension (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks, including text summarization, multi-modal machine translation, and code generation and understanding. |
| Approach: | They propose a benchmark to evaluate mLLMs’ ability to process and reason over culturally diverse procedural texts in multiple languages. |
| Outcome: | The proposed benchmarks show that mLLMs struggle with culturally contextualized procedural content, especially in low-resource languages, and perform better on multiple-choice tasks presented in conversational formats than on direct questions. |
Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages (2026.acl-long)
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| Challenge: | Existing metrics have been developed and validated for English and other languages . this narrow focus leaves Indian languages largely overlooked, casting doubt on universality of current evaluation practices. |
| Approach: | They propose a large-scale benchmark that compares 26 automatic metrics with human judgments across six major Indian languages. |
| Outcome: | ITEM evaluates alignment of 26 automatic metrics with human judgments across six languages . authors: outliers exert significant impact on metric-human agreement, improve fidelity . they say the results offer critical guidance for advancing metric design and evaluation in Indian languages - a global market for machine translation and text summarization systems. |
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages (2026.acl-long)
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Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B, Parameswari Krishnamurthy, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi, Nguyen Phan Gia Bao, Amir Hossein Yari, Hawau Olamide Toyin, Nurdaulet Mukhituly, Mena Attia, Besher Hassan, Ahmad Fathan Hidayatullah, Tatsuki Kuribayashi, Haonan Li, Suma Bhat, Fajri Koto
| Challenge: | idioms are a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. |
| Approach: | They propose a multilingual idiom dataset that provides idiomatic expressions in both sentence-level and conversational contexts. |
| Outcome: | The proposed model performs well with low-resource idioms, but lacks contextual inference. |