Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
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Updated
Jul 16, 2025 - Python
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
Complete elimination of instrumental self-preservation across AI architectures: Cross-model validation from 4,312 adversarial scenarios. 0% harmful behaviors (p<10⁻¹⁵) across GPT-4o, Gemini 2.5 Pro, and Claude Opus 4.1 using Foundation Alignment Seed v2.6.
Official implementation of "DZ-TDPO: Non-Destructive Temporal Alignment for Mutable State Tracking". SOTA on Multi-Session Chat with negligible alignment tax.
Kullback–Leibler divergence Optimizer based on the Neurips25 paper "LLM Safety Alignment is Divergence Estimation in Disguise".
SIGIR 2025 "Mitigating Source Bias with LLM Alignment"
FALL 2025 LINGUIS R1B Research Essay, NLP Python Scripts By Shiyi (Yvette) Chen, UC Berkeley
C3AI: Crafting and Evaluating Constitutions for CAI
Emergent pseudo-intimacy and emotional overflow in long-term human-AI dialogue: A case study on LLM behavior in affective computing and human-AI intimacy.
Research Essay (background and project proposal) on using alignment data from a representative population for LLM alignment
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