222 citations · 328 across the 139 of their papers we have counts for
48 papers · 1 filter
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…
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval
Aakash Mahalingam, Vinesh Kumar Gande, Aman Chadha +2
Retrieval-Augmented Generation (RAG) systems have become pivotal in leveraging vast corpora to generate informed and contextually relevant responses, notably reducing hallucination…
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Vennam +5
Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, comple…
Improving speaker verification robustness with synthetic emotional utterances
Nikhil Kumar Koditala, Chelsea Jui-Ting Ju, Ruirui Li +3
A speaker verification (SV) system offers an authentication service designed to confirm whether a given speech sample originates from a specific speaker. This technology has paved…
The Visual Counter Turing Test (VCT2): A Benchmark for Evaluating AI-Generated Image Detection and the Visual AI Index (VAI)
Nasrin Imanpour, Abhilekh Borah, Shashwat Bajpai +12
The rapid progress and widespread availability of text-to-image (T2I) generative models have heightened concerns about the misuse of AI-generated visuals, particularly in the conte…
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…
ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models
Vipula Rawte, Sarthak Jain, Aarush Sinha +9
Recent advances in Large Multimodal Models (LMMs) have expanded their capabilities to video understanding, with Text-to-Video (T2V) models excelling in generating videos from textu…
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…
DM-Codec: Distilling Multimodal Representations for Speech Tokenization
Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin +6
Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional…
MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation
Gurucharan Marthi Krishna Kumar, Aman Chadha, Janine Mendola +1
Large Language Models (LLMs), known for their versatility in textual data, are increasingly being explored for their potential to enhance medical image segmentation, a crucial task…
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge Types
Neelabh Sinha, Vinija Jain, Aman Chadha
Visual Question-Answering (VQA) has become key to user experience, particularly after improved generalization capabilities of Vision-Language Models (VLMs). But evaluating VLMs for…
Density Adaptive Attention-based Speech Network: Enhancing Feature Understanding for Mental Health Disorders
Georgios Ioannides, Adrian Kieback, Aman Chadha +1
Speech-based depression detection poses significant challenges for automated detection due to its unique manifestation across individuals and data scarcity. Addressing these challe…
Unboxing Occupational Bias: Grounded Debiasing of LLMs with U.S. Labor Data
Atmika Gorti, Manas Gaur, Aman Chadha
Large Language Models (LLMs) are prone to inheriting and amplifying societal biases embedded within their training data, potentially reinforcing harmful stereotypes related to gend…
Out-of-Distribution Detection with Attention Head Masking for Multimodal Document Classification
Christos Constantinou, Georgios Ioannides, Aman Chadha +2
Detecting out-of-distribution (OOD) data is crucial in machine learning applications to mitigate the risk of model overconfidence, thereby enhancing the reliability and safety of d…
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks
Niyar R Barman, Krish Sharma, Ashhar Aziz +7
The rapid advancement of text-to-image generation systems, exemplified by models like Stable Diffusion, Midjourney, Imagen, and DALL-E, has heightened concerns about their potentia…
Decoding the Diversity: A Review of the Indic AI Research Landscape
Sankalp KJ, Vinija Jain, Sreyoshi Bhaduri +2
This review paper provides a comprehensive overview of large language model (LLM) research directions within Indic languages. Indic languages are those spoken in the Indian subcont…
Cognitively Inspired Energy-Based World Models
Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam +3
One of the predominant methods for training world models is autoregressive prediction in the output space of the next element of a sequence. In Natural Language Processing (NLP), t…
MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention
Prince Jha, Raghav Jain, Konika Mandal +3
In the digital world, memes present a unique challenge for content moderation due to their potential to spread harmful content. Although detection methods have improved, proactive…
How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions
Julia Kharchenko, Tanya Roosta, Aman Chadha +1
Large Language Models (LLMs) attempt to imitate human behavior by responding to humans in a way that pleases them, including by adhering to their values. However, humans come from…
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
Devichand Budagam, Ashutosh Kumar, Mahsa Khoshnoodi +3
Assessing the effectiveness of large language models (LLMs) in performing different tasks is crucial for understanding their strengths and weaknesses. This paper presents Hierarchi…
Are Small Language Models Ready to Compete with Large Language Models for Practical Applications?
Neelabh Sinha, Vinija Jain, Aman Chadha
The rapid rise of Language Models (LMs) has expanded their use in several applications. Yet, due to constraints of model size, associated cost, or proprietary restrictions, utilizi…
Investigating Annotator Bias in Large Language Models for Hate Speech Detection
Amit Das, Zheng Zhang, Najib Hasan +12
Data annotation, the practice of assigning descriptive labels to raw data, is pivotal in optimizing the performance of machine learning models. However, it is a resource-intensive…
The Evolution of Multimodal Model Architectures
Shakti N. Wadekar, Abhishek Chaurasia, Aman Chadha +1
This work uniquely identifies and characterizes four prevalent multimodal model architectural patterns in the contemporary multimodal landscape. Systematically categorizing models…
Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha +2
The mining of adverse drug events (ADEs) is pivotal in pharmacovigilance, enhancing patient safety by identifying potential risks associated with medications, facilitating early de…