LLM Content Moderation and User Satisfaction: Evidence from Response Refusals in Chatbot Arena

Understand

LLM safety and ethical alignment are widely discussed, but the impact of content moderation on user satisfaction remains underexplored.

• In particular, little is known about how users respond when models refuse to answer a prompt-one of the primary mechanisms used to enforce ethical boundaries in LLMs.
• We address this gap by analyzing nearly 50,000 model comparisons from Chatbot Arena, a platform where users indicate their preferred LLM response in pairwise matchups, providing a large-scale setting for studying real-world user preferences.
• Using a novel RoBERTa-based refusal classifier fine-tuned on a hand-labeled dataset, we distinguish between refusals due to ethical concerns and technical limitations.