Margin-calibrated classifier guidance via Sequence Completion Ranking raises multi-step retrosynthesis solve rates from 16.8% to 95.3% on USPTO-190 and unlocks previously unsolvable targets.
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SAD modifies the denoising process in text diffusion models to enforce safety constraints at inference time, reducing unsafe generations while preserving quality and diversity.
Activation Addition steers language models by adding contrastive activation vectors from prompt pairs to control high-level properties like sentiment and toxicity at inference time without training.
citing papers explorer
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Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning
Margin-calibrated classifier guidance via Sequence Completion Ranking raises multi-step retrosynthesis solve rates from 16.8% to 95.3% on USPTO-190 and unlocks previously unsolvable targets.
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The Safety-Aware Denoiser for Text Diffusion Models
SAD modifies the denoising process in text diffusion models to enforce safety constraints at inference time, reducing unsafe generations while preserving quality and diversity.
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Steering Language Models With Activation Engineering
Activation Addition steers language models by adding contrastive activation vectors from prompt pairs to control high-level properties like sentiment and toxicity at inference time without training.