5 citations · 8 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models
Sina Bagheri Nezhad, Ameeta Agrawal, Rhitabrat Pokharel
Multilingual language models (MLLMs) are crucial for handling text across various languages, yet they often show performance disparities due to differences in resource availability…
Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
Sina Bagheri Nezhad, Sayan Bandyapadhyay, Ameeta Agrawal
Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to e…
Evaluating Multilingual Long-Context Models for Retrieval and Reasoning
Ameeta Agrawal, Andy Dang, Sina Bagheri Nezhad +2
Recent large language models (LLMs) demonstrate impressive capabilities in handling long contexts, some exhibiting near-perfect recall on synthetic retrieval tasks. However, these…
What Drives Performance in Multilingual Language Models?
Sina Bagheri Nezhad, Ameeta Agrawal
This study investigates the factors influencing the performance of multilingual large language models (MLLMs) across diverse languages. We study 6 MLLMs, including masked language…
Exploring the Maze of Multilingual Modeling
Sina Bagheri Nezhad, Ameeta Agrawal
Multilingual language models have gained significant attention in recent years, enabling the development of applications that meet diverse linguistic contexts. In this paper, we pr…
Smoking prevalence in Covid-19 patients
Sina Bagheri-Nezhad, Nasser Mozayani, Elham Abdi +1
We investigate the prevalence rate of smoking in Covid-19 patients and examine whether there is a difference in the distribution of smokers between the two statistical populations…