Papers by Saket Dingliwal
Few Shot Dialogue State Tracking using Meta-learning (2021.eacl-main)
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| Challenge: | Existing methods for transferring knowledge from resource-rich domains to unknown domains are data hungry . a meta-learning algorithm is proposed to solve the problem of zero/few-shot DST . |
| Approach: | They propose a meta-learner for the problem of zero/few-shot DST . they propose to agnostically train any existing chatbot system to improve its performance . |
| Outcome: | The proposed meta-learner improves on baseline in a low-data setting. |
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. |
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. |
Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization (2024.emnlp-industry)
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| Challenge: | Existing approaches to generate concise summarizations require extensive modifications to the architecture. |
| Approach: | They propose a lightweight model that can be finetuned to extract salient keyphrases from the source document to enhance ROUGE F1 and recall. |
| Outcome: | The proposed model can be finetuned to extract salient keyphrases without any LLM customization. |
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale (2023.acl-long)
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| Challenge: | 70% of attention heads and 20% of the feed forward networks can be removed with minimal decline in task performance. |
| Approach: | They propose to investigate whether in-context learning is not uniform across all components of a large language model. |
| Outcome: | The proposed model can remove 70% of attention heads and 20% of feed forward networks with minimal decline in task performance. |
Mamba Drafters for Speculative Decoding (2025.findings-emnlp)
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Daewon Choi, Seunghyuk Oh, Saket Dingliwal, Jihoon Tack, Kyuyoung Kim, Woomin Song, Seojin Kim, Insu Han, Jinwoo Shin, Aram Galstyan, Shubham Katiyar, Sravan Babu Bodapati
| Challenge: | Existing drafters that use external drafters suffer from slower drafting while self-speculation methods use drafters tailored to the target model but require re-training. |
| Approach: | They propose a drafter based on a state space model, Mamba, as a solution that combines the best aspects of both approaches. |
| Outcome: | The proposed drafters outperform existing drafters while using less memory and maintaining their cross-model adaptability. |
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. |