Papers by Karan Gupta
Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities (2025.findings-emnlp)
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| Challenge: | Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. |
| Approach: | They propose a framework that identifies and prompts users to enrich their posts by eliciting missing support attributes. |
| Outcome: | The proposed framework improves engagement and elicits missing information from posts. |
FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models (2025.emnlp-industry)
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Karan Dua, Hitesh Laxmichand Patel, Puneet Mittal, Ranjeet Gupta, Amit Agarwal, Praneet Pabolu, Srikant Panda, Hansa Meghwani, Graham Horwood, Fahad Shah
| Challenge: | Document understanding models require large, diverse, and well-annotated datasets that can cost millions of dollars to collect and maintain. |
| Approach: | They propose a scalable synthetic data generation framework that combines Stochastic Schemas and Parameterized Sampling to produce realistic, multilingual semi-structured documents with rich annotations. |
| Outcome: | Experiments on key information extraction tasks show that the proposed framework improves the absolute F1 score by up to 11% while reducing annotation effort by over 90% compared to traditional hard-template methods. |
SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models (2025.acl-industry)
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| Challenge: | Text-to-Speech (TTS) training requires extensive and diverse text and speech data. |
| Approach: | They propose a synthetic speech data generation pipeline that generates multilingual, domain-specific datasets for TTS training. |
| Outcome: | The proposed pipeline generates data that is 10–48% more diverse than baseline across various linguistic and phonetic metrics, along with speaker-standardized speech audio while generating approximately 97% correctly normalized text. |
MVTamperBench: Evaluating Robustness of Vision-Language Models (2025.findings-acl)
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Amit Agarwal, Srikant Panda, Angeline Charles, Hitesh Laxmichand Patel, Bhargava Kumar, Priyaranjan Pattnayak, Taki Hasan Rafi, Tejaswini Kumar, Hansa Meghwani, Karan Gupta, Dong-Kyu Chae
| Challenge: | Multimodal Large Language Models (MLLMs) have been a key advance in video understanding but their vulnerability to adversarial tampering remains underexplored. |
| Approach: | They evaluate MLLMs against five prevalent tampering techniques to assess their robustness . they use a tampered video format to examine the vulnerability of ML models . |
| Outcome: | The benchmark evaluates MLLMs against five prevalent tampering techniques based on 19 video manipulation tasks. |
Generative or Discriminative? Revisiting Text Classification in the Era of Transformers (2025.emnlp-main)
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Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury, Ashutosh Kumar, Yaswanth Biruduraju, Santhosh Kumar Kasa, Pattisapu Nikhil Priyatam, Arindam Bhattacharya, Shailendra Agarwal, Vijay Huddar
| Challenge: | generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, a trade-off that remains unexplored in the transformer era. |
| Approach: | They propose to evaluate generative and discriminative architectures for text classification using a generative model that learns the conditional probability distribution P (y|x) generative models are known to work better in low-data settings, giving rise to the classical 'two regimes' phenomenon for classification. |
| Outcome: | The proposed models show that the classical 'two regimes' manifests distinctly across different architectures and training paradigms. |
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques (2024.findings-acl)
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Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury, Pattisapu Priyatam, Anish Bhanushali, Prasanna Srinivasa Murthy
| Challenge: | Ordinal classification (OC) is a key task in natural language processing with applications in various domains such as sentiment analysis, rating prediction, and more. |
| Approach: | They propose to tackle ordinal classification (OC) through the implicit semantics of the labels . they propose to use a classical explicit approach and an implicit approach that organically engages the semantics. |
| Outcome: | The proposed methods are based on pre-trained language models and offer strategic recommendations based upon specific settings. |