12 citations · 27 across the 22 of their papers we have counts for
22 papers
Quantizing Intent: Cross-Domain Semantic IDs from Organic Activity for Industrial Ranking
Julie Choi, Haoran Ye, Zhiwei Ding +3
Ads click-through rate (CTR) prediction is constrained by sparse user supervision: most users engage with ads infrequently while generating dense behavioral evidence in organic sur…
4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič +52
The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy an…
Bridging the Semantic Gap: Contrastive Rewards for Multilingual Text-to-SQL with GRPO
Ashish Kattamuri, Ishita Prasad, Meetu Malhotra +3
Current Text-to-SQL methods are evaluated and only focused on executable queries, overlooking the semantic alignment challenge -- both in terms of the semantic meaning of the query…
Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data
Ashish Kattamuri, Arpita Vats, Harshwardhan Fartale +3
Recursive prompting with large language models enables scalable synthetic dataset generation but introduces the risk of bias amplification. We investigate gender bias dynamics acro…
Multimedia-Aware Question Answering: A Review of Retrieval and Cross-Modal Reasoning Architectures
Rahul Raja, Arpita Vats
Question Answering (QA) systems have traditionally relied on structured text data, but the rapid growth of multimedia content (images, audio, video, and structured metadata) has in…
RADAR: Mechanistic Pathways for Detecting Data Contamination in LLM Evaluation
Ashish Kattamuri, Harshwardhan Fartale, Arpita Vats +2
Data contamination poses a significant challenge to reliable LLM evaluation, where models may achieve high performance by memorizing training data rather than demonstrating genuine…
Disentangling Recall and Reasoning in Transformer Models through Layer-wise Attention and Activation Analysis
Harshwardhan Fartale, Ashish Kattamuri, Rahul Raja +3
Transformer-based language models excel at both recall (retrieving memorized facts) and reasoning (performing multi-step inference), but whether these abilities rely on distinct in…
Counterfactual Risk Minimization with IPS-Weighted BPR and Self-Normalized Evaluation in Recommender Systems
Rahul Raja, Arpita Vats
Learning and evaluating recommender systems from logged implicit feedback is challenging due to exposure bias. While inverse propensity scoring (IPS) corrects this bias, it often s…
Beyond Nearest Neighbors: Semantic Compression and Graph-Augmented Retrieval for Enhanced Vector Search
Rahul Raja, Arpita Vats
Vector databases typically rely on approximate nearest neighbor (ANN) search to retrieve the top-k closest vectors to a query in embedding space. While effective, this approach oft…
A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
Rahul Raja, Anshaj Vats, Arpita Vats +1
Recommender systems have traditionally followed modular architectures comprising candidate generation, multi-stage ranking, and re-ranking, each trained separately with supervised…
Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts
Rahul Raja, Arpita Vats
We investigate the generalization capabilities of small language models under two popular adaptation paradigms: few-shot prompting and supervised fine-tuning. While prompting is of…
Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations
Abhilekh Borah, Chhavi Sharma, Danush Khanna +12
Alignment is no longer a luxury, it is a necessity. As large language models (LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must r…
Parallel Corpora for Machine Translation in Low-resource Indic Languages: A Comprehensive Review
Rahul Raja, Arpita Vats
Parallel corpora play an important role in training machine translation (MT) models, particularly for low-resource languages where high-quality bilingual data is scarce. This revie…
FUSE : A Ridge and Random Forest-Based Metric for Evaluating MT in Indigenous Languages
Rahul Raja, Arpita Vats
This paper presents the winning submission of the RaaVa team to the AmericasNLP 2025 Shared Task 3 on Automatic Evaluation Metrics for Machine Translation (MT) into Indigenous Lang…
Multilingual State Space Models for Structured Question Answering in Indic Languages
Arpita Vats, Rahul Raja, Mrinal Mathur +2
The diversity and complexity of Indic languages present unique challenges for natural language processing (NLP) tasks, particularly in the domain of question answering (QA).To addr…
Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
Arpita Vats, Vinija Jain, Rahul Raja +1
The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional…
Recovering from Privacy-Preserving Masking with Large Language Models
Arpita Vats, Zhe Liu, Peng Su +5
Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typica…
Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers
Arpita Vats, Aman Chadha
The ability to recognize and interpret facial emotions is a critical component of human communication, as it allows individuals to understand and respond to emotions conveyed throu…
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Benjamin Kiefer, Matej Kristan, Janez Perš +70
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…
Estimation of Warfarin Dosage with Reinforcement Learning
Arpita Vats
In this paper, it has attempted to use Reinforcement learning to model the proper dosage of Warfarin for patients.The paper first examines two baselines: a fixed model of 35 mg/wee…
COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings
Manuel Morales, Rachel Barbar, Darshan Gandhi +27
COVID-19 testing, the cornerstone for effective screening and identification of COVID-19 cases, remains paramount as an intervention tool to curb the spread of COVID-19 both at loc…
Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks
Arpita Vats
In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, train…