5 citations · 15 across the 16 of their papers we have counts for
17 papers
SeRA: Self-Reviewing and Alignment of Large Language Models using Implicit Reward Margins
Jongwoo Ko, Saket Dingliwal, Bhavana Ganesh +3
Direct alignment algorithms (DAAs), such as direct preference optimization (DPO), have become popular alternatives for Reinforcement Learning from Human Feedback (RLHF) due to thei…
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models
Raghuveer Peri, Sai Muralidhar Jayanthi, Srikanth Ronanki +11
Integrated Speech and Large Language Models (SLMs) that can follow speech instructions and generate relevant text responses have gained popularity lately. However, the safety and r…
SpeechVerse: A Large-scale Generalizable Audio Language Model
Nilaksh Das, Saket Dingliwal, Srikanth Ronanki +14
Large language models (LLMs) have shown incredible proficiency in performing tasks that require semantic understanding of natural language instructions. Recently, many works have f…
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs
Sai Muralidhar Jayanthi, Devang Kulshreshtha, Saket Dingliwal +2
Personalization of automatic speech recognition (ASR) models is a widely studied topic because of its many practical applications. Most recently, attention-based contextual biasing…
Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages
Devang Kulshreshtha, Saket Dingliwal, Brady Houston +1
Connectionist Temporal Classification (CTC) models are popular for their balance between speed and performance for Automatic Speech Recognition (ASR). However, these CTC models sti…
Don't Stop Self-Supervision: Accent Adaptation of Speech Representations via Residual Adapters
Anshu Bhatia, Sanchit Sinha, Saket Dingliwal +3
Speech representations learned in a self-supervised fashion from massive unlabeled speech corpora have been adapted successfully toward several downstream tasks. However, such repr…
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale
Hritik Bansal, Karthik Gopalakrishnan, Saket Dingliwal +3
Language models have been shown to perform better with an increase in scale on a wide variety of tasks via the in-context learning paradigm. In this paper, we investigate the hypot…
Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting
Saket Dingliwal, Monica Sunkara, Sravan Bodapati +3
End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently. In these models, a non-autoregr…
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems
Saket Dingliwal, Ashish Shenoy, Sravan Bodapati +3
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memor…
Prompt-tuning in ASR systems for efficient domain-adaptation
Saket Dingliwal, Ashish Shenoy, Sravan Bodapati +3
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains. Since domain-specific systems perform better than their…
Few Shot Dialogue State Tracking using Meta-learning
Saket Dingliwal, Bill Gao, Sanchit Agarwal +3
Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc. With the…
Robust Handwriting Recognition with Limited and Noisy Data
Hai Pham, Amrith Setlur, Saket Dingliwal +7
Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that…
Covariate Distribution Aware Meta-learning
Amrith Setlur, Saket Dingliwal, Barnabas Poczos
Meta-learning has proven to be successful for few-shot learning across the regression, classification, and reinforcement learning paradigms. Recent approaches have adopted Bayesian…
Finding Input Characterizations for Output Properties in ReLU Neural Networks
Saket Dingliwal, Divyansh Pareek, Jatin Arora
Deep Neural Networks (DNNs) have emerged as a powerful mechanism and are being increasingly deployed in real-world safety-critical domains. Despite the widespread success, their co…
Advances in Symmetry Breaking for SAT Modulo Theories
Saket Dingliwal, Ronak Agarwal, Happy Mittal +1
Symmetry breaking is a popular technique to reduce the search space for SAT solving by exploiting the underlying symmetry over variables and clauses in a formula. The key idea is t…
Literature Survey on Finding Influential Communities in Large Scale Networks
Prakhar Ganesh, Saket Dingliwal, Rahul Agarwal
Community or modular structure is considered to be a significant property of large scale real-world graphs such as social or information networks. Detecting influential clusters or…
Restructuring Conversations using Discourse Relations for Zero-shot Abstractive Dialogue Summarization
Prakhar Ganesh, Saket Dingliwal
Dialogue summarization is a challenging problem due to the informal and unstructured nature of conversational data. Recent advances in abstractive summarization have been focused o…