Skip to content

Researcher Tools

Human Feedback and Eval Paper Explorer

A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

Total papers: 1 Search mode: keyword RSS
PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training

Yuhan Cheng, Hancheng Ye, Hai Helen Li, Jingwei Sun, Yiran Chen · Feb 14, 2026

Citations: 0
Pairwise Preference Automatic Metrics Multi Agent Coding
  • We propose PrivAct, a contextual privacy-aware multi-agent learning framework that internalizes contextual privacy preservation directly into models' generation behavior for privacy-compliant agentic actions.
  • Experiments across multiple LLM backbones and benchmarks demonstrate consistent improvements in contextual privacy preservation, reducing leakage rates by up to 12.32% while maintaining comparable helpfulness, as well as zero-shot…

Protocol Hubs

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.