Papers by Sai Muralidhar Jayanthi
Accelerated Test-Time Scaling with Model-Free Speculative Sampling (2025.emnlp-main)
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Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, Sravan Babu Bodapati
| Challenge: | Language models have demonstrated remarkable capabilities in reasoning tasks through test-time scaling techniques like best-of-N sampling and tree search. |
| Approach: | They propose a model-free speculative decoding approach that exploits redundancy in reasoning trajectories to achieve significant acceleration without compromising accuracy. |
| Outcome: | The proposed approach reduces inference latency by 60-65% while maintaining accuracy. |
NeuSpell: A Neural Spelling Correction Toolkit (2020.emnlp-demos)
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| Challenge: | a new spelling correction toolkit is available for free. |
| Approach: | They propose an open-source toolkit for spelling correction in English . they train neural models using spelling errors in context and using richer contextual representations. |
| Outcome: | The proposed spell-checker improves accuracy on synthetic examples and richer representations of the context. |
Think Clearly: Improving Reasoning via Redundant Token Pruning (2025.findings-emnlp)
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Daewon Choi, Jimin Lee, Jihoon Tack, Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, Sravan Babu Bodapati
| Challenge: | Recent large language models show promising capabilities in long-form reasoning . however, they tend to include substantial redundancy in reasoning paths . |
| Approach: | They propose a structure-aware pruning method that prioritizes removing redundant tokens . they remove redundant token and then resume the reasoning generation . |
| Outcome: | The proposed method shows strong performance on reasoning-intensive benchmarks without training. |
Constrained Fact Verification for FEVER (2020.emnlp-main)
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| Challenge: | Existing methods for fact verification rely on extracted evidence, but there is little work on understanding the reasoning process. |
| Approach: | They propose a method that enforces a closed-world reliance on extracted evidence to verify a claim's factuality. |
| Outcome: | The proposed model outperforms existing models on the FEVER shared task and shows that it is more accurate than previous models. |
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs (2023.emnlp-industry)
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| Challenge: | End-to-end ASR models struggle to recognize uncommon domain-specific words due to limited audio context. |
| Approach: | They propose a "Retrieve and Copy" mechanism to improve latency while retaining the accuracy even when scaled to a large catalog. |
| Outcome: | The proposed method achieves 6% more word error rate reduction and 3.6% improvement in F1 when scaled to a large catalog size while retaining the accuracy. |
SpeechGuard: Exploring the Adversarial Robustness of Multi-modal Large Language Models (2024.findings-acl)
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Raghuveer Peri, Sai Muralidhar Jayanthi, Srikanth Ronanki, Anshu Bhatia, Karel Mundnich, Saket Dingliwal, Nilaksh Das, Zejiang Hou, Goeric Huybrechts, Srikanth Vishnubhotla, Daniel Garcia-Romero, Sundararajan Srinivasan, Kyu Han, Katrin Kirchhoff
| Challenge: | Integrated Speech and Large Language Models (SLMs) that follow speech instructions and generate relevant text responses have gained popularity lately. |
| Approach: | They propose algorithms that can generate adversarial examples to jailbreak SLMs without human involvement. |
| Outcome: | The proposed algorithms achieve state-of-the-art on spoken question-answering task scoring over 80% on both safety and helpfulness metrics. |