Overdue job update — I am now: - A Visiting Scientist at @schmidtsciences.bsky.social, supporting AI safety & interpretability - A Visiting Researcher at Stanford NLP Group, working with @cgpotts.bsky.social So grateful to keep working in this fascinating area—and to start supporting others too :)

📢 Speakers Announcement #AthNLP2025 We’re thrilled to welcome a new lineup of brilliant minds to the ATHNLP stage!🚀 Meet our new NLP speakers shaping the future. 📅 Dates: 4-10 September 2025 athnlp.github.io/2025/speaker... #ATHNLP #NLP #AI #MachineLearning #Athens

Looking forward to this year's edition! With great speakers: Ryan McDonald Yulan He @vn-ml.bsky.social @antonisa.bsky.social Raquel Fernandez @annarogers.bsky.social Preslav Nakov @mohitbansal.bsky.social @eunsol.bsky.social Marie-Catherine de Marnefffe !

🔥 Huge CONGRATS to Jaemin + @jhucompsci.bsky.social! 🎉 Very proud of his journey as an amazing researcher (covering groundbreaking, foundational research on important aspects of multimodality+other areas) & as an awesome, selfless mentor/teamplayer 💙 -- Apply to his group & grab him for gap year!

🚨 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.

🔥 BIG CONGRATS to Elias (and UT Austin)! Really proud of you -- it has been a complete pleasure to work with Elias and see him grow into a strong PI on *all* axes 🤗 Make sure to apply for your PhD with him -- he is an amazing advisor and person! 💙

🌵 I'm going to be presenting PBT at #NAACL2025 today at 2PM! Come by poster session 2 if you want to hear about: -- balancing positive and negative persuasion -- improving LLM teamwork/debate -- training models on simulated dialogues With @mohitbansal.bsky.social and @peterbhase.bsky.social

I will be presenting ✨Reverse Thinking Makes LLMs Stronger Reasoners✨at #NAACL2025! In this work, we show - Improvements across 12 datasets - Outperforms SFT with 10x more data - Strong generalization to OOD datasets 📅4/30 2:00-3:30 Hall 3 Let's chat about LLM reasoning and its future directions!

✈️ Heading to #NAACL2025 to present 3 main conf. papers, covering training LLMs to balance accepting and rejecting persuasion, multi-agent refinement for more faithful generation, and adaptively addressing varying knowledge conflict. Reach out if you want to chat!

Check out 🚨CAPTURe🚨 -- a new benchmark testing spatial reasoning by making VLMs count objects under occlusion. SOTA VLMs (GPT-4o, Qwen2-VL, Intern-VL2) have high error rates on CAPTURe (but humans have low error ✅) and models struggle to reason about occluded objects. arxiv.org/abs/2504.15485 🧵👇

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.

🚨Real-world retrieval is messy: queries are ambiguous or docs conflict & have incorrect/irrelevant info. How can we jointly address these problems? ➡️RAMDocs: challenging dataset w/ ambiguity, misinformation & noise ➡️MADAM-RAG: multi-agent framework, debates & aggregates evidence across sources 🧵⬇️

Excited to share my first paper as first author: "Task-Circuit Quantization" 🎉 I led this work to explore how interpretability insights can drive smarter model compression. Big thank you to @esteng.bsky.social, Yi-Lin Sung, and @mohitbansal.bsky.social for mentorship and collaboration. More to come

What if we could transform advanced math problems into abstract programs that can generate endless, verifiable problem variants? Presenting EFAGen, which automatically transforms static advanced math problems into their corresponding executable functional abstractions (EFAs). 🧵👇

🚨Announcing TaCQ 🚨 a new mixed-precision quantization method that identifies critical weights to preserve. We integrate key ideas from circuit discovery, model editing, and input attribution to improve low-bit quant., w/ 96% 16-bit acc. at 3.1 avg bits (~6x compression) 📃 arxiv.org/abs/2504.07389

🚨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! ⚡

🎉🎉 Big congrats to @archiki.bsky.social on being awarded the @Apple AI/ML PhD Fellowship, for her extensive contributions in evaluating+improving reasoning in language/reward models and their applications to new domains (ReCEval, RepARe, System-1.x, ADaPT, ReGAL, ScPO, UTGen, GrIPS)! #ProudAdvisor

We had a blast at the NLP@Michigan Day! Many thanks to our outstanding keynote speakers Parisa Kordjamshidi & @mohitbansal.bsky.social, our fantastic organizers Muhammad Khalifa & Siyang Liu, and all the engaged participants from across campus and beyond. So much positive NLP energy! 💛💙

🚨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!

🚨 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. 🧵👇

🚨 Check out "UTGen & UTDebug" for learning to automatically generate unit tests (i.e., discovering inputs which break your code) and then applying them to debug code with LLMs, with strong gains (>12% pass@1) across multiple models/datasets! (see details in 🧵👇) 1/4

🚨 Excited to announce UTGen and UTDebug, where we first learn to generate unit tests and then apply them to debugging generated code with LLMs, with strong gains (+12% pass@1) on LLM-based debugging across multiple models/datasets via inf.-time scaling and cross-validation+backtracking! 🧵👇

🚨 Excited to share: "Learning to Generate Unit Tests for Automated Debugging" 🚨 which introduces ✨UTGen and UTDebug✨ for teaching LLMs to generate unit tests (UTs) and debugging code from generated tests. UTGen+UTDebug yields large gains in debugging (+12% pass@1) & addresses 3 key questions: 🧵👇

🎉 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 ... 🧵

🎉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

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...

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 💙

✨ Collaborating with our amazing postdocs in our lab over the past year has been a great learning experience, with lots of fun + exciting research in LLM agents, reasoning, & multimodality! Check out the new postdoc openings and become a part of the vibrant research @unccs.bsky.social !⬇️

Check out our postdoc openings at UNC-CH! In addition to the valuable research opportunities, please also note that Chapel Hill and the research triangle are great places to live, with nice weather, food, and people! I truly have enjoyed living here 😊

Apply soon to - work with tons of extremely talented and strong students and get hands-on experience mentoring - benefit from expert mentorship - be in a great department and live in an area with high quality of life and more! Feel free to ping me if you want to hear more from a current postdoc!

🚨 We have postdoc openings at UNC 🙂 Exciting+diverse NLP/CV/ML topics**, freedom to create research agenda, competitive funding, very strong students, mentorship for grant writing, collabs w/ many faculty+universities+companies, superb quality of life/weather. Please apply + help spread the word 🙏

I'm now at #NeurIPS2024! 🔥 (yeah, I'm the one with the red Santa hat🧑‍🎄) On Dec 13 PM, I present SELMA, co led with Jialu Li! 👉 improving the faithfulness of T2I models with automatically generated image-text pairs, with skill-specific expert learning and merging! P.S. I'm on the faculty job market👇

Recruiting reviewers + ACs for ACL 2025 in Interpretability and Analysis of NLP Models - DM me if you are interested in emergency reviewer/AC roles for March 18th to 26th - Self-nominate for positions here (review period is March 1 through March 20): docs.google.com/forms/d/e/1F...

✈️ I've landed in Vancouver for #NeurIPS2024 11/12: LACIE, a pragmatic speaker-listener method for training LLMs to express calibrated confidence: arxiv.org/abs/2405.21028 12/12: GTBench, a benchmark for game-theoretic abilities in LLMs: arxiv.org/abs/2402.12348 P.s. I'm on the faculty market👇

Thanks @mohitbansal.bsky.social for the wonderful Distinguished Lecture on agents and multimodal generation. This got so many of us here at Stony Brook excited for the potential in these areas. Also, thanks for spending time with our students & sharing your wisdom. It was a pleasure hosting you!

I was so lucky to work with Jaemin in my 1st year and learned a lot from him. I can confidently say he's not only a top mind in multimodal AI but also an incredible mentor&collaborator. He is insightful, hands-on, and genuinely knows how to guide and inspire junior students👇👏

Look out for @jmincho.bsky.social's application! He’s absolutely one of the top multimodal minds, I’ve been a fan of his work even before joining UNC and have been lucky enough to see him in action as a top-notch researcher and mentor!

I first met Jaemin at the Microsoft Accelerate Foundation Models Research (AFMR) dinner during NeurIPS 2023, though I was already familiar with his papers long before that. He’s definitely a strong candidate to consider!

Since I stared my PhD in 2021 I've seen Jaemin's papers and always found them really cool and impactful!

🚨 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)! 👇🧵

I've truly enjoyed ✨ all of our collaborations ✨ over the past year. I particularly admire his thoughtful ideas, dedication to seeing them through, and his mentorship of junior students to do the same. I'm excited to see research from his lab as a professor and an advisor! 😄

Dear schools -- Catch Elias! Definitely one of the best experts of AI agents. Working with him has been always a joy 🤗

🚨 I am on the faculty job market this year 🚨 I will be presenting at #NeurIPS2024 and am happy to chat in-person or digitally! I work on developing AI agents that can collaborate and communicate robustly with us and each other. More at: esteng.github.io and in thread below 🧵👇

Looking forward to giving this Distinguished Lecture at StonyBrook next week & meeting the several awesome NLP + CV folks there - thanks Niranjan‬ + all for the kind invitation 🙂 PS. Excited to give a new talk on "Planning Agents for Collaborative Reasoning and Multimodal Generation" ➡️➡️ 🧵👇

Reverse Thinking Makes LLMs Stronger Reasoners abs: arxiv.org/abs/2411.19865 Train an LLM to be able to generate forward reasoning from question, backward question, and backward reaoning from backward question Shows an average 13.53% improvement over the student model’s zero-shot performance

🚨Just read an exciting new paper from Justin 👇 on how to get the benefits of reasoning backwards (i.e. starting with the solution and tracing it back to the question) into a model while keeping the test-time cost of reasoning the same! 🧵1/3

🚨 Reverse Thinking Makes LLMs Stronger Reasoners We can often reason from a problem to a solution and also in reverse to enhance our overall reasoning. RevThink shows that LLMs can also benefit from reverse thinking 👉 13.53% gains + sample efficiency + strong generalization (on 4 OOD datasets)!

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...

We are hiring! + extremely collegial department with strong focus in AI. + RTP (Raleigh, durham, chapel hill) is one of the nicest place to live - diverse and inclusive!

APPLY: TENURE-TRACK/TEACHING FACULTY. Research areas include but not limited to: ML, NLP, vision, graphics, systems, bioinformatics, security, medical imaging, robotics, RT systems, AR/VR. Join our team—committed to research, teaching, and collaboration. ➡️https://cs.unc.edu/faculty-hiring/