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Vaidehi Patil

@vaidehipatil.bsky.social
952 followers 152 following 27 posts

Ph.D. Student at UNC NLP | Prev: Apple, Amazon, Adobe (Intern) vaidehi99.github.io | Undergrad @IITBombay

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Peter Hall @peterha2l.bsky.social · 15/07/2025
In any case, the work is featuring at an interesting-looking workshop this weekend, put on by @katherinelee.bsky.social, @vaidehipatil.bsky.social, and others. More info here: mugenworkshop.github.io
mugenworkshop.github.io
MUGen @ ICML 2025 - Workshop on Machine Unlearning for Generative AI
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Elias Stengel-Eskin @esteng.bsky.social · 05/05/2025
Extremely excited to announce that I will be joining @utaustin.bsky.social Computer Science in August 2025 as an Assistant Professor! 🎉
UT Austin campus
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
Thanks to my amazing collaborators Yi-Lin Sung , @peterbhase.bsky.social , Jie Peng, Tianlong Chen , @mohitbansal.bsky.social for a wonderful collaboration!
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
📎 Check it out here! 📄 Paper: arxiv.org/abs/2505.01456 💻 Code and Dataset: github.com/Vaidehi99/Un... huggingface.co/datasets/vai... 🤗 HuggingFace: huggingface.co/papers/2505....
arxiv.org
Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation
LLMs trained on massive datasets may inadvertently acquire sensitive information such as personal details and potentially harmful content. This risk is further heightened in multimodal LLMs as they in...
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
Key Findings 🔥 Multimodal attacks are the most effective 🛡️ Our strongest defense is deleting info from hidden states 📉 Larger models are more robust to extraction attacks post-editing compared to smaller ones 🎯 UnLOK-VQA enables targeted evaluations of unlearning defenses
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
⚔️ Benchmarking Multimodal Unlearning Defenses Multimodal data opens up new attack vectors. We benchmark 6 unlearning defenses against 7 attack strategies, including: ✅White-box attacks ✅Black-box paraphrased multimodal prompts
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
This enables two key types of evaluation: ✅Generalization Evaluation ✔️Rephrased questions ✔️Rephrased images ✅Specificity Evaluation ✔️Neighboring questions (same image, new question) ✔️Neighboring images (same concept, different image)
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
📦 What Is UnLOK-VQA? UnLOK-VQA focuses on unlearning pretrained knowledge and builds on OK-VQA, a visual QA dataset. We extend it w/ an automated question-answer generation and image generation pipeline: ✅Forget samples from OK-VQA ✅New samples at varying levels of proximity (easy, medium, hard)
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
This is essential for: 📜 Legal compliance (e.g., GDPR, CCPA, the right to be forgotten) 🔐 Multimodal Privacy (e.g., faces, locations, license plates) 📷 Trust in real-world image-grounded systems
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
🔍 Why Does Multimodal Unlearning Matter? Existing unlearning benchmarks focus only on text. But multimodal LLMs are trained on web-scale data—images + captions—making them highly vulnerable to leakage of sensitive or unwanted content. Unlearning must hold across modalities, not just in language.
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
We study: ❓ How effectively can we erase multimodal knowledge? ❓ How should we measure forgetting in multimodal settings? ✅We benchmark 6 unlearning defenses against 7 whitebox and blackbox attack strategies
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Vaidehi Patil @vaidehipatil.bsky.social · 07/05/2025
🚨 Introducing our @tmlrorg.bsky.social paper “Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation” We present UnLOK-VQA, a benchmark to evaluate unlearning in vision-and-language models, where both images and text may encode sensitive or private information.
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Mohit Bansal @mohitbansal.bsky.social · 21/04/2025
In Singapore for #ICLR2025 this week to present papers + keynotes 👇, and looking forward to seeing everyone -- happy to chat about research, or faculty+postdoc+phd positions, or simply hanging out (feel free to ping)! 🙂 Also meet our awesome students/postdocs/collaborators presenting their work.
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Katherine Lee @katherinelee.bsky.social · 02/04/2025
Come chat about unlearning with us!!
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Vaidehi Patil @vaidehipatil.bsky.social · 02/04/2025
Call for PC Members! We’re looking for program committee members! 📝 Submit your Expression of Interest here: forms.gle/ZPEHeymJ4t5N... #ICML2025
forms.gle
MUGen @ ICML '25 - PC Expression of Interest
We are currently recruiting reviewers for the Program Committee of MUGen (Machine Unlearning for Generative AI) @ ICML '25. If you are interested in participating, please fill out this form. We antici...
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Vaidehi Patil @vaidehipatil.bsky.social · 02/04/2025
👩‍💻 Organizers: Mantas Mazeika, Yang Liu, @katherinelee.bsky.social, @mohitbansal.bsky.social, Bo Li and myself (@vaidehipatil.bsky.social) 🙂
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Vaidehi Patil @vaidehipatil.bsky.social · 02/04/2025
🔥 Speakers & Panelists: We're lucky to have an incredible lineup of speakers and panelists covering diverse topics in our workshop: Nicholas Carlini, Ling Liu, Shagufta Mehnaz, @peterbhase.bsky.social , Eleni Triantafillou, Sijia Liu, @afedercooper.bsky.social, Amy Cyphert
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Vaidehi Patil @vaidehipatil.bsky.social · 02/04/2025
We invite contributions exploring key challenges and advancements at the intersection of machine unlearning and generative AI! 🔗 Full details & updates: mugenworkshop.github.io 📅 Key Dates: 📝 Submission Deadline: May 19 ✅ Acceptance Notifications: June 9 🤝 Workshop Date: July 18 or 19
mugenworkshop.github.io
MUGen @ ICML 2025 - Workshop on Machine Unlearning for Generative AI
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Vaidehi Patil @vaidehipatil.bsky.social · 02/04/2025
🚨Exciting @icmlconf.bsky.social workshop alert 🚨 We’re thrilled to announce the #ICML2025 Workshop on Machine Unlearning for Generative AI (MUGen)! ⚡Join us in Vancouver this July to dive into cutting-edge research on unlearning in generative AI with top speakers and panelists! ⚡
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Archiki Prasad @archiki.bsky.social · 27/03/2025
🥳🥳 Honored and grateful to be awarded the 2025 Apple Scholars in AI/ML PhD Fellowship! ✨ Huge shoutout to my advisor @mohitbansal.bsky.social, & many thanks to my lab mates @unccs.bsky.social , past collaborators + internship advisors for their support ☺️🙏 machinelearning.apple.com/updates/appl...
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Elias Stengel-Eskin @esteng.bsky.social · 25/02/2025
🚨UPCORE is our new method for balancing unlearning/forgetting with maintaining model performance. Best part is it works by selecting a coreset from the data rather than changing the model, so it is compatible with any unlearning method, with consistent gains for 3 methods + 2 tasks!
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
Huge thanks to my co-authors @esteng.bsky.social , and @mohitbansal.bsky.social for a great collaboration! 🚀 Check it out here: 📄 Paper: arxiv.org/abs/2502.15082 💻 Code: github.com/Vaidehi99/UP... 🤗 @huggingface page: huggingface.co/papers/2502....
arxiv.org
UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning
User specifications or legal frameworks often require information to be removed from pretrained models, including large language models (LLMs). This requires deleting or "forgetting" a set of data poi...
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
UPCORE consistently outperforms baselines across all methods: ✔️ Less unintended degradation ✔️ Deletion transferred to pruned points UPCORE provides a practical, method-agnostic approach that improves the reliability of unlearning techniques.
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
Instead of evaluating at a single training checkpoint, we introduce AUC (Area Under the Curve) across deletion effectiveness and utility. This provides a complete picture of the trade-off between forgetting and knowledge retention over the unlearning trajectory.
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
We apply UPCORE across three unlearning methods: 📉 Gradient Ascent 🚫 Refusal 🔄 Negative Preference Optimization (NPO) We measure: ✔️ Deletion effectiveness – How well the target is removed ✔️ Unintended degradation – Impact on other abilities ✔️ Positive transfer – How well unlearning generalizes
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
Even after pruning, the pruned points in the forget set still become unlearned -- thanks to positive collateral transfer from the core forget set. Thus, UPCORE reduces negative collateral effects while maintaining effective deletion.
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
UPCORE constructs a core forget set by identifying and removing outlier points using Isolation Forest. ✅ Minimizes unintended degradation ✅ Preserves model utility ✅ Compatible with multiple unlearning methods
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
Our key insight: Not all forget set points degrade the model equally. Points contributing to high variance cause more collateral damage when unlearned. By pruning these outliers, UPCORE reduces unintended forgetting while ensuring effective deletion.
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
LLMs train on vast datasets, often with sensitive or unwanted info. Regulations like GDPR, CCPA mandate removal. Yet, standard unlearning can degrade unrelated knowledge, making it unreliable. Effective unlearning is key for: 📜 Compliance (GDPR, CCPA) 🔐 Privacy & security ⚖️ Ethical AI development
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Vaidehi Patil @vaidehipatil.bsky.social · 25/02/2025
🚨 Introducing UPCORE, to balance deleting info from LLMs with keeping their other capabilities intact. UPCORE selects a coreset of forget data, leading to a better trade-off across 2 datasets and 3 unlearning methods. 🧵👇
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Mohit Bansal @mohitbansal.bsky.social · 27/01/2025
🎉 Congrats to the awesome students, postdocs, & collaborators for this exciting batch of #ICLR2025 and #NAACL2025 accepted papers (FYI some are on the academic/industry job market and a great catch 🙂), on diverse, important topics such as: -- adaptive data generation environments/policies ... 🧵
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Elias Stengel-Eskin @esteng.bsky.social · 23/01/2025
🎉Very excited that our work on Persuasion-Balanced Training has been accepted to #NAACL2025! We introduce a multi-agent tree-based method for teaching models to balance: 1️⃣ Accepting persuasion when it helps 2️⃣ Resisting persuasion when it hurts (e.g. misinformation) arxiv.org/abs/2410.14596 🧵 1/4
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Vaidehi Patil @vaidehipatil.bsky.social · 21/01/2025
Congratulations to my advisor, Mohit! 🎉 So excited to see his impactful contributions to AI honored with the #AAAI Fellow recognition, coming right after the prestigious #PECASE award. Well-earned and inspiring—congrats! 🙂
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Mohit Bansal @mohitbansal.bsky.social · 21/01/2025
Thanks @AAAI for selecting me as a #AAAI Fellow! Very humbled+excited to be a part of the respected cohort of this+past years' fellows (& congrats everyone)! 🙏 100% credit goes to my amazing past/current students+postdocs+collab for their work (& thanks to mentors+family)!💙 aaai.org/about-aaai/a...
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Mohit Bansal @mohitbansal.bsky.social · 15/01/2025
Deeply honored & humbled to have received the Presidential #PECASE Award by the @WhiteHouse and @POTUS office! 🙏 Most importantly, very grateful to my amazing mentors, students, postdocs, collaborators, and friends+family for making this possible, and for making the journey worthwhile + beautiful 💙
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Vaidehi Patil @vaidehipatil.bsky.social · 11/12/2024
Jaemin is an expert in multimodal AI, and his practical and insightful suggestions have always been incredibly helpful to me. I’m confident that he will continue to achieve great things in his career!
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Jaemin Cho @jmincho.bsky.social · 07/12/2024
🚨 I’m on the academic job market! j-min.io I work on ✨Multimodal AI✨, advancing reasoning in understanding & generation by: 1⃣ Making it scalable 2⃣ Making it faithful 3⃣ Evaluating + refining it Completing my PhD at UNC (w/ @mohitbansal.bsky.social). Happy to connect (will be at #NeurIPS2024)! 👇🧵
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Abhilasha Ravichander @lasha.bsky.social · 11/11/2024
✨I am on the faculty job market in the 2024-2025 cycle!✨ My research centers on advancing Responsible AI, specifically enhancing factuality, robustness, and transparency in AI systems. If you have relevant positions, let me know! lasharavichander.github.io Please share/RT!
lasharavichander.github.io
Abhilasha Ravichander - Home
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Vaidehi Patil @vaidehipatil.bsky.social · 06/12/2024
Working with Elias has been an absolute pleasure! His passion for research and dedication to mentoring are inspiring. Can’t wait to see all the amazing work his lab will do as he becomes a professor! ✨
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UNC-Chapel Hill Computer Science @unccs.bsky.social · 21/11/2024
Congratulations to #UNC CS student David Wan for winning the prestigious 2024 Google PhD Fellowship in NLP. 🎉🥳 A very well-deserved honor for his impactful work on factual and faithful text+multimodal generation with Prof. @mohitbansal.bsky.social and UNC NLP group! ▶️ blog.google/technology/r...
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