Arshia Hemmat

Arshia Hemmat

Multimodal and trustworthy AI researcher with a focus on generative models and zero‑shot learning

Posts by Collection

portfolio

publications

CLIP Exhibits Improved Compositional Generalization Through Representation Disentanglement

Published in Preprint (ICLR 2024 submission), 2023

Investigates how the compositional out‑of‑distribution generalization of CLIP models emerges from training data diversity and representation disentanglement. Demonstrates that richer attribute–object combinations in the training set lead to improved performance and that disentangling image and text representations enhances compositional generalization.

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Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision‑Language Models

Published in NeurIPS 2024 (Datasets & Benchmarks Track), 2024

Introduces IllusionBench, a dataset that hides letters, faces and animals inside everyday scenes to audit whether modern vision‑language models can recognize abstract shapes. Human subjects achieve near‑perfect accuracy on the tasks, whereas state‑of‑the‑art models score below 40 % zero‑shot, revealing significant robustness gaps.

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Leveraging Retrieval‑Augmented Generation for Persian University Knowledge Retrieval

Published in 15th IKT (accepted – oral), 2024

Proposes a two‑stage retrieval‑augmented generation pipeline that combines Persian large language models with tailored prompt engineering to answer university‑related queries. Introduces the UniversityQuestionBench dataset and evaluates performance using faithfulness, answer relevance and context relevance metrics.

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Context Awareness Gate for Retrieval‑Augmented Generation

Published in 15th IKT (accepted), 2024

Introduces the Context Awareness Gate (CAG), a mechanism that dynamically decides whether a query requires external context retrieval in a retrieval‑augmented generation pipeline. Includes a vector‑candidates method for scalable, LLM‑independent semantic search and demonstrates that skipping unnecessary retrieval improves answer quality.

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MEENA (PersianMMMU): Multimodal‑Multilingual Educational Exams for N‑level Assessment

Published in Under review (COLM 2025), 2025

Presents the first large‑scale Persian multimodal benchmark for evaluating vision‑language models on scientific reasoning, problem‑solving and human‑level understanding. Contains 7,500 Persian and 3,000 English multimodal questions with rich metadata such as difficulty, descriptive answers and student success rates and evaluates GPT‑4, Gemini and other models under zero‑shot, few‑shot and hallucination detection settings.

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talks

Zero-shot Learning in Medical Domain

Published:

Focusing on developing models capable of recognizing unseen medical conditions from imaging data, this research tackles the challenge of improving diagnostic tools without extensive labeled datasets. Dr. Peyman Adibi guides the project, aiming to revolutionize diagnostic methodologies through zero-shot learning.

Text Summarization Using Graph Neural Network

Published:

Under Dr. Hamidreza Baradaran’s guidance, this project employs graph neural networks for efficient text summarization. The objective is to enhance the coherence and relevance of automated summaries, facilitating improved information retrieval and understanding across large text corpora.

Vision-Linguistic Models

Published:

This project aims to enhance the synergy between visual perception and language processing in AI systems, exploring the boundaries of machine understanding and interpretation of complex visual-textual information. The work under Prof. Mohammad Hossein Rohban investigates innovative methodologies to advance the capabilities of vision-linguistic models.

3D Vessel Segmentation Using Geometric Approaches

Published:

This research focuses on leveraging geometric deep learning techniques for the accurate segmentation of blood vessels in 3D medical images. The aim is to enhance diagnostic and therapeutic procedures in medicine, showcasing the potential of geometric approaches in complex image segmentation tasks.

Counterfactual Modelling Using Vision-Linguistic Models

Published:

Investigates generating counterfactual scenarios through integration of visual and linguistic data to enhance AI interpretability and reliability, focusing on applications such as autonomous driving and medical diagnostics. This research highlights the potential of vision-linguistic models in creating diverse, hypothetical scenarios for advanced problem-solving and decision-making processes.

teaching

Deep Learning

Graduate course, Sharif University of Technology, Department of EE, 2023

Machine Learning

undergraduate course, University of Isfahan, Department of CE, 2023