Our group is recruiting a postdoc!
📢I'm recruiting a postdoc at Rice University! Looking for someone with a PhD in CS (or a related field) and expertise in LLMs and Trustworthy & Responsible AI.
I'll be at #COLM2025 from 10/8-10/10. Happy to chat in person!
Feel free to email or DM me if you're interested.😀
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We’re thrilled to present our latest work at ICML 2025! Distribution-wise Intervention is a simple yet powerful technique that learns intervention regions. Come talk to us at ICML if you’re interested—we’d love to chat! #ICML2025
[#ICML2025] If steering vector can be multiplied by a scalar to control its effect, why it should be a vector instead of a steering "region"?
We introduce distribution-wise intervention, a simple method that directly learns the latent intervention region through reparameterization. Given a deterministic intervention function (mean network), we equip it with an independent network (logvar network) to learn its surrounding region.
🎄 Key Takeaway:
- D-Intervention is effective at early layers, achieving a drop-in gain compared to its deterministic counterparts at 2% to 4% improvement.
- The downstream performance is pretty correlated w/ its learned variance, the more it can explore its surrounding, the more it could achieve better results.
- Controlling test-time stochasticity to fulfill the need for creativity in different tasks! Very fun to play with 😀
🚀 Thrilled to announce SAFR is here! #NAACL2025
Superposition is powerful — but it buries interpretability. We control that!
🧠 Neurons often mix too many features (superposition) — making models a black box.
🎯 SAFR strategically redistributes neurons:
· 🧩 Monosemantic for important tokens
· 🔗 Polysemantic where interactions matter
🔥 SAFR controls how neurons represent features — without sacrificing accuracy.
💬 Excited to present this work at #NAACL2025 Findings — come chat with us!
