Robust reward models are critical for alignment/inference-time algos, auto eval, etc. (e.g. to prevent reward hacking which could render alignment ineffective). ⚠️ But we found that SOTA RMs are brittle 🫧 and easily flip predictions when the inputs are slightly transformed 🍃 🧵

Like human brains, large language models reason about diverse data in a general way. A new study shows LLMs represent different data types based on their underlying meaning & reason about data in their dominant language: bit.ly/3QrZvyy

To appear @ #ICLR2025! We show that LMs represent semantically-equivalent inputs across languages, modalities, etc. similarly. This shared representation space is structured by the LM's dominant language, which is also relevant to recent phenomena where LMs "think" in Chinese🀄️ in English🔠 contexts

We have released our code at github.com/ZhaofengWu/s.... We hope that this could be useful for future studies understanding the how LMs work!

excited to be at #NeurIPS2024! I'll be presenting our data mixture inference attack 🗓️ Thu 4:30pm w/ @jon.jon.ke — stop by to learn what trained tokenizers reveal about LLM development (‼️) and chat about all things tokenizers. 🔗 arxiv.org/abs/2407.16607

31% of US adults use generative AI for healthcare 🤯But most AI systems answer questions assertively—even when they don’t have the necessary context. Introducing #MediQ a framework that enables LLMs to recognize uncertainty🤔and ask the right questions❓when info is missing: 🧵

💡We find that models “think” 💭 in English (or in general, their dominant language) when processing distinct non-English or even non-language data types 🤯 like texts in other languages, arithmetic expressions, code, visual inputs, & audio inputs‼️ 🧵⬇️ arxiv.org/abs/2411.04986

🚨New dataset + challenge🚨 We release ASL STEM Wiki: the first signing dataset of STEM articles! 📰 254 Wikipedia articles 📹 ~300 hours of ASL interpretations 👋 New task: automatic sign suggestion to make STEM education more accessible microsoft.com/en-us/resear... 🧵 #EMNLP2024