Papers by Tejaswini Pedapati
STAR: Spectral Truncation and Rescale for Model Merging (2025.naacl-short)
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| Challenge: | Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning. |
| Approach: | They propose a model merging technique that aims at mitigating "merging conflicts" by truncating small components in the respective spectral spaces and then an automatic parameter rescaling scheme to retain the nuclear norm of the original matrix. |
| Outcome: | The proposed model outperforms baseline models on flan-T5 by 4.2% and is robust to hyperparamater choice. |
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models (2024.findings-acl)
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Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurelie Lozano, Payel Das, Georgios Kollias
| Challenge: | Transformer-based Language Models have become ubiquitous in natural language processing due to impressive performance on various tasks. |
| Approach: | They explore how sparsity affects network topology by exploiting mechanisms seen in biological networks . they show that model-agnostic sparsities are performant across diverse NLP tasks . |
| Outcome: | The proposed model-agnostic sparsity approaches are performant and efficient across NLP tasks. |
OjaKV: Context-Aware Online Low-Rank KV Cache Compression (2026.findings-acl)
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Yuxuan Zhu, David H. Yang, Mohammad Mohammadi Amiri, Keerthiram Murugesan, Tejaswini Pedapati, Pin-Yu Chen
| Challenge: | Existing methods for inference use static, offline-learned subspaces that perform poorly under distribution shifts. |
| Approach: | They propose a framework that integrates a storage policy with an online subspace adaptation to preserve key-value tokens in full rank as high-fidelity anchors. |
| Outcome: | Experiments show that OjaKV maintains or improves zero-shot accuracy at high compression ratios, achieving the strongest gains on long-context benchmarks requiring complex reasoning. |
ZoomR: Memory Efficient Reasoning through Multi-Granularity Key Value Retrieval (2026.acl-long)
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David H. Yang, Yuxuan Zhu, Mohammad Mohammadi Amiri, Keerthiram Murugesan, Tejaswini Pedapati, Subhajit Chaudhury, Pin-Yu Chen
| Challenge: | Large language models generate long chain of thoughts but memory footprint grows with output length . prior work on KV cache optimization focused on compressing long input context . |
| Approach: | They propose a new approach that compresses verbose reasoning thoughts into summaries . they use a dynamic KV cache selection policy that leverages these summary keys . |
| Outcome: | The proposed approach reduces memory usage while avoiding full-cache attention at each step. |
Granite Guardian: Comprehensive LLM Safeguarding (2025.naacl-industry)
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Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia, Subhajit Chaudhury, Tejaswini Pedapati, Pierre Dognin, Keerthiram Murugesan, Erik Miehling, Martín Santillán Cooper, Kieran Fraser, Giulio Zizzo, Muhammad Zaid Hameed, Mark Purcell, Michael Desmond, Qian Pan, Inge Vejsbjerg, Elizabeth M. Daly, Michael Hind, Werner Geyer, Ambrish Rawat, Kush R. Varshney, Prasanna Sattigeri
| Challenge: | a suite of advanced models is designed to detect and mitigate risks associated with prompts and responses. |
| Approach: | a team of researchers develop a model family to detect and mitigate risks associated with prompts and responses. the model family is based on the Granite 3.0 language models. |
| Outcome: | a new model family is designed to detect and mitigate risks associated with prompts and responses. |
EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts (2025.acl-long)
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Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan, Georgios Kollias, Elliot Nelson, Khushbu Pahwa, Tejaswini Pedapati, Igor Melnyk, Matthew Riemer
| Challenge: | Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks, but efficient processing of long contexts remains a significant challenge. |
| Approach: | They propose a method for processing long contexts in an episodic memory module while holistically attending to semantically-relevant context chunks. |
| Outcome: | The proposed method outperforms baseline decoders on multiple long-context recall and question-answering benchmarks on 16k to 256k tokens. |
AI Steerability 360: A Toolkit for Steering Large Language Models (2026.acl-demo)
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Erik Miehling, Karthikeyan Natesan Ramamurthy, Praveen Venkateswaran, Ching-Yun Ko, Pierre Dognin, Moninder Singh, Tejaswini Pedapati, Avinash Balakrishnan, Matthew Riemer, Dennis Wei, Inge Vejsbjerg, Elizabeth M. Daly, Kush R. Varshney
| Challenge: | The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. |
| Approach: | The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. |
| Outcome: | The toolkit is available under an Apache 2.0 license and is available on https://github.com/IBM/AISteer360. |