Hongli Zhan
@hongli-zhan.bsky.social
learning @IFM_MBZUAI, Silicon Valley Lab // 🤘Ph.D. @UTAustin
What does a scientific figure make you wonder? 📊 We introduce MQUD: multimodal Questions Under Discussion for scientific figures. With 1,250 author-annotated questions over 245 figures from 56 papers, MQUD asks what scientific question a figure raises in context.
1k+ downloads each on the MINT empathy models since release 🔥 Encouraging to see the interest in our work! tl;dr: In multi-turn empathic dialogue, LLMs reuse the same discourse moves far more often than humans do; MINT uses RL to diversify them. Give it a try!👇 huggingface.co/hongli-zhan/...
Hongli Zhan@hongli-zhan.bsky.social · 4mo agoNew paper! 🏁 Last one from my PhD at UT Austin. LLMs sound empathic but repeat the same discourse moves turn after turn — at 2x the rate of humans. We built MINT🌿, the first RL framework for discourse move diversity in empathic dialogue. +25% empathy, −26% repetition. 📄 arxiv.org/abs/2604.11742
In multi-turn conversation, LLMs tend to repeat the same kind of things over and over again. They could have different words, but we found them to be the *same discourse moves*! Introducing @hongli-zhan.bsky.social’s new work: novel discourse-level diversity rewards in post-training:
Hongli Zhan@hongli-zhan.bsky.social · 4mo agoNew paper! 🏁 Last one from my PhD at UT Austin. LLMs sound empathic but repeat the same discourse moves turn after turn — at 2x the rate of humans. We built MINT🌿, the first RL framework for discourse move diversity in empathic dialogue. +25% empathy, −26% repetition. 📄 arxiv.org/abs/2604.11742
New paper! 🏁 Last one from my PhD at UT Austin. LLMs sound empathic but repeat the same discourse moves turn after turn — at 2x the rate of humans. We built MINT🌿, the first RL framework for discourse move diversity in empathic dialogue. +25% empathy, −26% repetition. 📄 arxiv.org/abs/2604.11742
I'll be at #ICML to present SPRI next week! Come by our poster on Tuesday, July 15, 4:30pm, and let’s catch up on LLM alignment! 😃 🚀TL;DR: We introduce Situated-PRInciples (SPRI), a framework that automatically generates input-specific principles to align responses — with minimal human effort. 🧵
I’m excited to share that our paper has been accepted at #ICML2025! 🎉🥳🎊 This work was done during my internship at IBM Research, and it wouldn’t have been possible without a top-notch team and my amazing advisor 👏
Hongli Zhan@hongli-zhan.bsky.social · 2y agoConstitutional AI works great for aligning LLMs, but the principles can be too generic to apply. Can we guide responses with context-situated principles instead? Introducing SPRI, a system that produces principles tailored to each query, with minimal to no human effort. arxiv.org/pdf/2502.03397
To appear #ICML2025!! 🎉
Hongli Zhan@hongli-zhan.bsky.social · 2y agoConstitutional AI works great for aligning LLMs, but the principles can be too generic to apply. Can we guide responses with context-situated principles instead? Introducing SPRI, a system that produces principles tailored to each query, with minimal to no human effort. arxiv.org/pdf/2502.03397
The principles that LLMs align with should be specific to the task at hand! Check out @hongli-zhan.bsky.social’s latest work 👇
Hongli Zhan@hongli-zhan.bsky.social · 2y agoConstitutional AI works great for aligning LLMs, but the principles can be too generic to apply. Can we guide responses with context-situated principles instead? Introducing SPRI, a system that produces principles tailored to each query, with minimal to no human effort. arxiv.org/pdf/2502.03397
Constitutional AI works great for aligning LLMs, but the principles can be too generic to apply. Can we guide responses with context-situated principles instead? Introducing SPRI, a system that produces principles tailored to each query, with minimal to no human effort. arxiv.org/pdf/2502.03397