Srija Mukhopadhyay

Hey! I am Srija, a dual-degree student pursuing my Bachelor’s and Master’s in Computer Science at IIIT Hyderabad. I’m interested in Machine Learning research and have always loved languages in general. I’ve been lucky enough to work with amazing professors like Manish Shrivastava, Ponnurangam Kumaraguru, Karthik Vaidhyanathan, Vivek Gupta, and Dan Roth, and I even got to collaborate with Adobe Research at one point.

Lately, I’ve been really curious about Multimodal Models - especially when it comes to understanding their capabilities and making them more robust. Apart from that, I am also interested in Interpretability and AI Safety. I’m particularly passionate about figuring out how to make models - especially multimodal models - more transparent and interpretable, and also understanding their inner workings better.

I’ve also interned at Google twice - once as a STEP Intern and later as a SWE Intern, where I worked in the GCP Metering Team and Android PDF team respectively. I will be joining Google as an intern for the third time this summer!

Outside of work, I love diving into DIY projects. You’ll also find me listening to indie music, swimming, at the gym, or spending hours watching videos of Japanese stationery hauls. I also love to travel and explore new places. I’m always up for a good conversation, so feel free to reach out to me!

selected publications

EMNLP

Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness

Srija Mukhopadhyay, Adnan Qidwai, Aparna Garimella, and 3 more authors

In Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Abs PDF Website

Chart question answering (CQA) is a crucial area of Visual Language Understanding. However, the robustness and consistency of current Visual Language Models (VLMs) in this field remain under-explored. This paper evaluates state-of-the-art VLMs on comprehensive datasets, developed specifically for this study, encompassing diverse question categories and chart formats. We investigate two key aspects: 1) the models’ ability to handle varying levels of chart and question complexity, and 2) their robustness across different visual representations of the same underlying data. Our analysis reveals significant performance variations based on question and chart types, highlighting both strengths and weaknesses of current models. Additionally, we identify areas for improvement and propose future research directions to build more robust and reliable CQA systems. This study sheds light on the limitations of current models and paves the way for future advancements in the field.
PrePrint

MAPWise: Evaluating Vision-Language Models for Advanced Map Queries

Srija Mukhopadhyay, Abhishek Rajgaria, Prerana Khatiwada, and 2 more authors

2024

Abs PDF

Vision-language models (VLMs) excel at tasks requiring joint understanding of visual and linguistic information. A particularly promising yet under-explored application for these models lies in answering questions based on various kinds of maps. This study investigates the efficacy of VLMs in answering questions based on choropleth maps, which are widely used for data analysis and representation. To facilitate and encourage research in this area, we introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China), each containing 1000 questions. Our benchmark incorporates 43 diverse question templates, requiring nuanced understanding of relative spatial relationships, intricate map features, and complex reasoning. It also includes maps with discrete and continuous values, encompassing variations in color-mapping, category ordering, and stylistic patterns, enabling comprehensive analysis. We evaluate the performance of multiple VLMs on this benchmark, highlighting gaps in their abilities and providing insights for improving such models.