A survey-style position paper that restates existing control techniques (prompt tuning, LoRA, ROME, PPLM) and asserts, without proof or data, that minimal weight edits enable high-successful steering.
Parameter-efficient transfer learning for NLP
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Manipulating Transformer-Based Models: Controllability, Steerability, and Robust Interventions
A survey-style position paper that restates existing control techniques (prompt tuning, LoRA, ROME, PPLM) and asserts, without proof or data, that minimal weight edits enable high-successful steering.