A central question in alignment research concerns how language models acquire, represent, and arbitrate between competing values. The Helpful, Harmless, Honest (HHH) framework proposed by Askell et al. [33] formalizes alignment as the joint optimization of multiple normative objectives through supervised fine-tuning and reinforcement learning from human feedback. Building on this paradigm, Bai et al. [34] demonstrates that models can be trained to navigate tensions between helpfulness and harmlessness, and that larger models exhibit improved robustness in resolving such trade-offs under distributional shift.
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These documents were retrieved from the SAILDART repository overseen by Bruce Baumgart, specifically from the AM segment curated by Douglas Lenat, accessible via: https://www.saildart.org/[AM,DBL]/