Distributed Systems @ AWS

Graduate Research Student with Professor Dan Brown at the University of Waterloo. My work encompassed analyzing the capabilities and limitations of LLMs (Large Language Models), especially open-source models, in instruction following and answering questions about sensitive topics. Traditional Natural Language Processing (NLP) benchmarks often overlook nuances in LLM behavior and reliability. My thesis addresses this gap by curating a dataset across six categories: Fact, Conspiracy, Controversy, Misconception, Stereotype, and Fiction. We rigorously define LLMs' factual accuracy, consistency, and robustness to prompt variations using diverse response formats and question variations, and evaluate these on 37 models. Our findings reveal LLMs' volatility and unreliability, particularly in the Controversy and Misconception categories, where conflicting training data impedes performance. Additionally, we explore LLMs' ability to generate coherent fictional narratives, probing their ability to retain and effectively utilize factual information, a critical requirement for creative tasks like story generation. While LLMs offer versatile applications, their reliability hinges on addressing challenges in prompt understanding and response consistency, Thesis: * https://uwspace.uwaterloo.ca/items/e01e11a6-e033-4f6a-85c6-849fba74e039Dataset:* https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/5MZWBV GitHub:* https://github.com/tanny411/llm-reliability-and-consistency-evaluation Media Coverage:* https://ediscoverytoday.com/2024/08/30/uncovering-the-reliability-and-consistency-of-ai-language-models-artificial-intelligence-trends/Publications:* Reliability Check: An Analysis of GPT-3’s Response to Sensitive Topics and Prompt Wording (https://aclanthology.org/2023.trustnlp-1.8/)* A Study on Large Language Models' Limitations in Multiple-Choice Question Answering (https://arxiv.org/abs/2401.07955)

- Teaching Assistant CS135 Designing Functional Programs - student.cs.uwaterloo.ca/~cs135- Teaching Assistant CS240 (twice) Data Structures and Data Management - student.cs.uwaterloo.ca/~cs240/w23- Instructional Apprentice CS105 Introduction to Computer Programming 1 - https://student.cs.uwaterloo.ca/~cs105- Teaching Assistant CS230 Introduction to Computers and Computer Systems - https://student.cs.uwaterloo.ca/~cs230/s24/index.shtml

- Worked on improving the Wikipedia link recommendation system in all 300+ Wikipedia languages by creating a small suite of language-agnostic models to handle all languages with high precision. This helps address the deployment and testing bottlenecks due to large number of models, as well as will improve link recommendation in small wikis.* https://meta.wikimedia.org/wiki/Research:Improving_multilingual_support_for_link_recommendation_model_for_add-a-link_task- Worked with the Research Team to develop Copyediting as a structured task. To increase and maintain the standard of Wikipedia articles, it is important to ensure articles don't have typos, spelling, or grammatical errors. While there are ongoing efforts to automatically detect "commonly misspelled" words in English Wikipedia, most other languages are left behind. I built a pipeline to automatically curate and detect commonly misspelled words in 100+ languages in Wikipedia using the entirely of Wiktionary and Wikipedia.* https://meta.wikimedia.org/wiki/Research:Copyediting_as_a_structured_task

I worked on analyzing Wikidata Query Service (https://query.wikidata.org/) queries and Wikidata dumps as a contract data analyst to help figure out ways to scale the service. My analysis included- Understanding Wikidata's structure, what it consists of, and how diverse it is- Finding subgraphs within Wikidata, automating the subgraph detection workflow, and generating various subgraph metrics- Finding ways to identify SPARQL queries that access certain subgraphs and generate subgraph query metrics- Productionizing the analysis work on Wikidata subgraphs metrics and subgraph query metricsSee my work here: https://wikitech.wikimedia.org/w/index.php?title=User:AKhatun

Worked on the Abstract Wikipedia Data Science project to find important modules and show modules similar to each other in order to merge or refactor modules towards a language-independent Wikipedia.- Extensively used SQL and MediawikiAPI to collect, group, and analyze Lua modules across all 300+ language Wikipedias- Used various Unsupervised Machine Learning algorithms to cluster the collected modules, identify similar modules, and isolate unique modules.- Created a tool using Vue.JS to display the similar and unique modules along with similarity scores and several filtering mechanisms.Web-interface: abstract-wiki-ds.toolforge.orgSee our work in GitHub @ github.com/wikimedia/abstract-wikipedia-data-scienceand Phabricator @ phabricator.wikimedia.org/T263678

- Used computer vision for accurate face detection in images and video footage for in-office use.- Researched and applied smaller yet accurate pre-trained Computer Vision models suitable for IoT devices.- Leveraged OCR to extract measurements from pulse oximeters images for swift COVID-19 detection.- Implemented an ML-based fall detection system using inertial sensor readings from smartwatches.

Projects:* Language Agnostic Source Code Authorship Attribution* Character Level Authorship Attribution in Bengali Literature* Transfer-Learning-based approach to Attribution in Bengali Literature. * Collected long-text Authorship Attribution Dataset. * Collected multiple large Bengali corpus. * Pre-trained and fine-tuned ULMFiT and mBERT from scratch. * Assessed the effectiveness of pre-training datasets and tokenizations on downstream Authorship Attribution task.Publications:* Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT (https://dl.acm.org/doi/abs/10.1145/3530691)* Authorship Attribution in Bangla literature using Character-level CNN (https://ieeexplore.ieee.org/abstract/document/9038560)* A Subword Level Language Model for Bangla Language (https://link.springer.com/chapter/10.1007/978-981-15-3607-6_31)