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20212026
most citedWhat Drives Performance in Multilingual Language Models?

5 citations · 8 across the 12 of their papers we have counts for

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12 papers

cs.CL2026

Toward Culturally Grounded Natural Language Processing

Sina Bagheri Nezhad

Multilingual NLP is often treated as a route to global inclusion, but linguistic coverage and cultural competence frequently diverge. This paper synthesizes over 50 papers spanning…

cs.AI2026

Signals Are Not States: Neuro-Symbolic Safeguards for Culturally Aware Classroom AI

Sina Bagheri Nezhad

Classroom AI systems increasingly infer high-level educational states such as engagement, confusion, collaboration, participation, and instructional quality from multimodal and lin…

cs.CL2025

SymCode: A Neurosymbolic Approach to Mathematical Reasoning via Verifiable Code Generation

Sina Bagheri Nezhad, Yao Li, Ameeta Agrawal

Large Language Models (LLMs) often struggle with complex mathematical reasoning, where prose-based generation leads to unverified and arithmetically unsound solutions. Current prom…

cs.CL2025

Enhancing Large Language Models with Neurosymbolic Reasoning for Multilingual Tasks

Sina Bagheri Nezhad, Ameeta Agrawal

Large language models (LLMs) often struggle to perform multi-target reasoning in long-context scenarios where relevant information is scattered across extensive documents. To addre…

cs.DS2025

Polynomial-Time Constant-Approximation for Fair Sum-of-Radii Clustering

Sina Bagheri Nezhad, Sayan Bandyapadhyay, Tianzhi Chen

In a seminal work, Chierichetti et al. introduced the -fair clustering problem: Given a set of red points and a set of blue points in a metric space, a clustering is called…

cs.CL2025

The Impact of Model Scaling on Seen and Unseen Language Performance

Rhitabrat Pokharel, Sina Bagheri Nezhad, Ameeta Agrawal +1

The rapid advancement of Large Language Models (LLMs), particularly those trained on multilingual corpora, has intensified the need for a deeper understanding of their performance…