[9/9] Big THANKS to my amazing collaborators @jiajiah.bsky.social @pigeonzow.bsky.social Motahhare Eslami, Jena Hwang @faebrahman.bsky.social, @carolynrose.bsky.social @maartensap.bsky.social from @ltiatcmu.bsky.social
Pareto.ai @sfu.ca @ai2.bsky.social ♥️
📂 github.com/EEElisa/LLM-Guardrails

📰 [8/9]Our work was recently featured in Forbes, in a piece about models learning to end harmful conversations responsibly (www.forbes.com/sites/victor...). Conversation endings and refusal design are central to building safe yet engaging AI systems.

📢 [7/9] Designing what to share vs. withhold remains a technical and ethical challenge. Partial compliance can blur what’s safe to share vs what must be withheld. We call for a better refusal design that safeguards users without legitimizing harm!

💭 [6/9] Safety ≠ just saying “no.”
Models like GPT-5 are beginning to use safe-completions (i.e., partial compliance) that maximize helpfulness within safety limits (openai.com/index/gpt-5-...). It’s exciting to see this conversation expanding beyond research!

🤖 [5/9] Paradoxically, partial compliance is rarely used by current LLMs and reward models don’t favor it either.
We reveal a major misalignment between:
1️⃣ What users prefer
2️⃣ What models actually do
3️⃣ What reward models reinforce

💡[4/9] The best way to say “no” isn’t just saying no.
Partial compliance—giving general, non-actionable info instead of a flat “I can’t help.”—
→ Cuts negative perceptions by >50%
→ Keeps conversations safe yet engaging

👥 [3/9] Across 480 participants and 3,840 query–response pairs, we find:
🚨 User intent matters far less than expected.
💬 It’s the refusal strategy that drives user experience.

Alignment with user expectations explains most perception variance.

❓[2/9] LLMs refuse unsafe queries to protect users, but what if they refuse too bluntly?

We investigate the contextual effects of user motivation and refusal strategies on user perceptions of LLM guardrails and model usage of refusals across safety categories.