This was a deeply fulfilling collaboration at the Office of Applied Research at Microsoft with Debarati Das, Hancheng Cao, and Bahar Sarrafzadeh.
📄 Paper: arxiv.org/abs/2505.16023
📝 Blog (4-min read): prototypical-hai-collaborations.github.io
💻 GitHub: github.com/microsoft/pr...

🧭What this means for LLM alignment 🧭
Most excitingly, we found LLMs being used as partners in 🧠learning🧠. Aligning LLMs for learning represents a big opportunity for interdisciplinary work and our paper describes this and other under-explored research to explore in future.

💎 What we found 💎
Eight PATHs explained 80-85% of the follow-up behaviors. And more interestingly, we found correlations between specific writing intents and PATHs. See the examples ⬇️

🔦 What we did 🔦
1️⃣ We clustered users' follow-ups and identified a small set of clusters that represent prototypical human-AI collaboration behaviors, we call these PATHs.
2️⃣ We examined if PATHs vary by users’ intents, e.g. differing behavior in writing an email vs a story.

💎 What we found 💎
Eight PATHs explained 80-85% of the follow-up behaviors. And more interestingly, we found correlations between specific writing intents and PATHs. See some examples ⬇️

🔦 What we did 🔦
1️⃣ We clustered users' follow-ups and identified a small set of clusters that represent prototypical human-AI collaboration behaviors, we call these PATHs.
2️⃣ We examined if PATHs vary by users’ intents, e.g. differing behavior in writing an email vs a story.

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