Papers by Bhanukiran Vinzamuri
LUME: LLM Unlearning with Multitask Evaluations (2025.findings-emnlp)
Copied to clipboard
Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, Rahul Gupta
| Challenge: | Unlearning aims to remove copyrighted, sensitive, or private content from large language models without a full retraining. |
| Approach: | They propose a multi-task unlearning benchmark LUME that unlearns short novels, biographies and public biographie . |
| Outcome: | The proposed benchmark unlearns short novels, biographies and public biographie . it also releases fine-tuned models with 1B and 7B parameter sizes as targets . |
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate (2025.naacl-long)
Copied to clipboard
Xiaomeng Jin, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Mingyi Hong
| Challenge: | Existing methods to remove unwanted knowledge from large language models are formulated as minimizing memorization through the loss of the model. |
| Approach: | They propose a normalized gradient difference algorithm that optimizes a forgetting objective and an automatic learning rate scheduler that allows for better control over the trade-off between the objectives. |
| Outcome: | The proposed method improves on TOFU and MUSE datasets while exhibiting stable training. |
Adversarial Robustness for Large Language NER models using Disentanglement and Word Attributions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) tasks are becoming more challenging due to the introduction of complex tagsets, which often leads to the failure of existing NER systems in accurately recognizing these entities. |
| Approach: | They propose a novel attack which relies on disentanglement and word attribution techniques to learn an embedding and identifying important words across both components. |
| Outcome: | The proposed approach improves the F1 score over the original LLM model by 8% and 18% on CoNLL-2003 and Ontonotes 5.0 datasets respectively. |
BLUR: A Bi-Level Optimization Approach for LLM Unlearning (2026.eacl-long)
Copied to clipboard
Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Sijia Liu, Mingyi Hong
| Challenge: | Existing algorithms to unlearn knowledge and capabilities from large datasets are unclear how to best formulate the unlearning problem. |
| Approach: | They propose to model the hierarchical structure of the unlearning problem, where the forget problem takes priority over the retain problem, and propose an algorithm that aims to unlearn knowledge and capabilities. |
| Outcome: | The proposed algorithm outperforms all state-of-the-art algorithms across unlearning tasks, models, and metrics. |