Training-Free Task Vectors for LLM Behavioral Control
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TL;DR - Training-Free Task Vectors (TFTVs) convert activation-steering directions into rank-one weight edits using only forward-pass statistics, eliminating task-vector fine-tuning. The method enables composable behavioral control while preserving general model capabilities.
- TFTVs support behavior amplification through addition, suppression through subtraction, and composition of multiple edits.
- The approach maps activation-steering vectors into weight space without model fine-tuning.
- Across LLM behavioral-control tasks, TFTVs consistently controlled target traits while retaining general knowledge and problem-solving skills.
- Compared with editing and steering baselines, TFTVs delivered stronger trait control with competitive or better utility preservation.
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Training-Free Task Vectors for LLM Behavioral Control
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TL;DR - Training-Free Task Vectors (TFTVs) convert activation-steering directions into rank-one weight edits using only forward-pass statistics, eliminating task-vector fine-tuning. The method enables composable behavioral control while preserving general model capabilities.
- TFTVs support behavior amplification through addition, suppression through subtraction, and composition of multiple edits.
- The approach maps activation-steering vectors into weight space without model fine-tuning.
- Across LLM behavioral-control tasks, TFTVs consistently controlled target traits while retaining general knowledge and problem-solving skills.
- Compared with editing and steering baselines, TFTVs delivered stronger trait control with competitive or better utility preservation.