Shujian Zhang
@zhang_shujian
Research Scientist @GoogleDeepmind | Ph.D. @UTAustin
قد يعجبك
I am hiring a Student Researcher at Google DeepMind for 2025! 👩🔬 Interested in improving multi-turn optimization and reasoning capabilities of LLMs? 🧑🎓 Currently studying for a Bachelor's/Master's/PhD? 🧑💻 Have solid engineering and research skills? 🌟We want to hear from you!
How can LLM architecture recognize Instruction Hierarchy? 🚀 Excited to share our latest work on Instructional Segment Embedding (ISE)! A technique embeds Instruction Hierarchy directly into LLM architecture, significantly boosting LLM safety. 🧵[1/n]
![TongWu_Pton's tweet image. How can LLM architecture recognize Instruction Hierarchy?
🚀 Excited to share our latest work on Instructional Segment Embedding (ISE)! A technique embeds Instruction Hierarchy directly into LLM architecture, significantly boosting LLM safety. 🧵[1/n]](https://pbs.twimg.com/media/GaB-XkuXsAAcxbU.png)
📝 For more details and experiments, please check our arxiv: arxiv.org/abs/2410.09102 Thanks to all of my amazing collaborators 🙌 @WenxuanZhou_96 @zhang_shujian @SongKaiqiang @sileixu @ChongXiang7 Prof. @prateekmittal_ and more colleagues from @zoom. 🧵[7/n]
🚨 Excited to share our new preprint with @zhang_shujian, @WenxuanZhou_96, @MarzyehGhassemi, and Sanqiang Zhao. 🚀 TLDR: We propose SFTMix, a novel recipe that elevates language model instruction tuning without relying on expensive, well-curated datasets. arxiv.org/abs/2410.05248
Introducing WPO: Enhancing RLHF with Weighted Preference Optimization 🌟 Our new preference optimization method reweights preference data to simulate on-policy preference optimization using off-policy data, combining efficiency with high performance. ✅ up to 5.6% better than…
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