273 citations · 329 across the 34 of their papers we have counts for
35 papers
Findings of the Counter Turing Test: AI-Generated Image Detection
Rajarshi Roy, Nasrin Imanpour, Ashhar Aziz +16
The rapid advancements in generative AI technologies, such as Stable Diffusion, DALL-E, and Midjourney, have significantly transformed the creation of synthetic visual content. Whi…
Findings of the Counter Turing Test: AI-Generated Text Detection
Rajarshi Roy, Gurpreet Singh, Ashhar Aziz +16
The growing capability of large language models to produce fluent, contextually coherent text has created mounting pressure on the systems and institutions responsible for ensuring…
A Comprehensive Dataset for Human vs. AI Generated Image Detection
Rajarshi Roy, Ashhar Aziz, Shashwat Bajpai +17
Multimodal generative AI systems like Stable Diffusion, DALL-E, and MidJourney have fundamentally changed how synthetic images are created. These tools drive innovation but also en…
Learning from Generalization Patterns: An Evaluation-Driven Approach to Enhanced Data Augmentation for Fine-Tuning Small Language Models
Huan Song, Deeksha Razdan, Yiyue Qian +6
Small Language Models (SLMs) offer compelling advantages in deployment cost and latency, but their accuracy often lags behind larger models, particularly for complex domain-specifi…
DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images
Dwip Dalal, Gautam Vashishtha, Anku Rani +11
The rise in harmful online content not only distorts public discourse but also poses significant challenges to maintaining a healthy digital environment. In response to this, we in…
A Comprehensive Dataset for Human vs. AI Generated Text Detection
Rajarshi Roy, Gurpreet Singh, Ashhar Aziz +17
The rapid advancement of large language models (LLMs) has led to increasingly human-like AI-generated text, raising concerns about content authenticity, misinformation, and trustwo…
LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs
Rushendra Sidibomma, Pransh Patwa, Parth Patwa +3
The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research…
Overview of Factify5WQA: Fact Verification through 5W Question-Answering
Suryavardan Suresh, Anku Rani, Parth Patwa +6
Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news fo…
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
Ronit Singhal, Pransh Patwa, Parth Patwa +2
Given the widespread dissemination of misinformation on social media, implementing fact-checking mechanisms for online claims is essential. Manually verifying every claim is very c…
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan +2
The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This…
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data
Parth Patwa, Simone Filice, Zhiyu Chen +3
Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. In-Context Learning (ICL) typically achieves better…
CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling
Mohsin Ali, Kandukuri Sai Teja, Neeharika Gupta +5
The mixing of two or more languages is called Code-Mixing (CM). CM is a social norm in multilingual societies. Neural Language Models (NLMs) like transformers have been effective o…
Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring
Ankitha Sudarshan, Vinay Samuel, Parth Patwa +2
Automatic Speech Recognition (ASR) has witnessed a profound research interest. Recent breakthroughs have given ASR systems different prospects such as faithfully transcribing spoke…
Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes
Shreyash Mishra, S Suryavardan, Megha Chakraborty +9
Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi-modal form of content wields in shaping online discourse. Memes have become a powerfu…
Findings of Factify 2: Multimodal Fake News Detection
S Suryavardan, Shreyash Mishra, Megha Chakraborty +9
With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent. The detrimental impact of fake news emphasizes the need for rese…
FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering
Megha Chakraborty, Khushbu Pahwa, Anku Rani +15
Combating disinformation is one of the burning societal crises -- about 67% of the American population believes that disinformation produces a lot of uncertainty, and 10% of them k…
IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
Varuna Krishna, S Suryavardan, Shreyash Mishra +6
Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis "You shall know a word by the company it keep…
Factify 2: A Multimodal Fake News and Satire News Dataset
S Suryavardan, Shreyash Mishra, Parth Patwa +9
The internet gives the world an open platform to express their views and share their stories. While this is very valuable, it makes fake news one of our society's most pressing pro…
Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes
Shreyash Mishra, S Suryavardan, Parth Patwa +9
Memes are the new-age conveyance mechanism for humor on social media sites. Memes often include an image and some text. Memes can be used to promote disinformation or hatred, thus…
Memotion Analysis through the Lens of Joint Embedding
Nethra Gunti, Sathyanarayanan Ramamoorthy, Parth Patwa +1
Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such ke…
PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages
Mohsin Ali, Kandukuri Sai Teja, Sumanth Manduru +2
NLP applications for code-mixed (CM) or mix-lingual text have gained a significant momentum recently, the main reason being the prevalence of language mixing in social media commun…
Offense Detection in Dravidian Languages using Code-Mixing Index based Focal Loss
Debapriya Tula, Shreyas MS, Viswanatha Reddy +5
Over the past decade, we have seen exponential growth in online content fueled by social media platforms. Data generation of this scale comes with the caveat of insurmountable offe…
Vaccination Worldwide: Strategies, Distribution and Challenges
Chirag Samal, Kasia Jakimowicz, Krishnendu Dasgupta +26
The Coronavirus 2019 (Covid-19) pandemic caused by the SARS-CoV-2 virus represents an unprecedented crisis for our planet. It is a bane of the über connected world that we live in…
Can Self Reported Symptoms Predict Daily COVID-19 Cases?
Parth Patwa, Viswanatha Reddy, Rohan Sukumaran +6
The COVID-19 pandemic has impacted lives and economies across the globe, leading to many deaths. While vaccination is an important intervention, its roll-out is slow and unequal ac…