IC-566Adding instruction-specific steering vectors to the residual stream improves instruction-following accuracy for Phi-3, Gemma 2 2B IT, Mistral 7B IT, and Gemma 2 9B IT across format, length, and word-specific constraints
Steering vectors computed as the difference in residual stream activations between inputs with and without an instruction are added at a single layer during inference. Without explicit text instructions, instruction-following accuracy rises from approximately 10% to approximately 30% across all four models. With explicit instructions (baseline 60-90%), steering produces a significant boost for two of the four models. For length constraints, increasing the steering weight c continuously shortens outputs, and steering with c=20 significantly improves adherence to maximum-sentence constraints (n=1..5). For word inclusion, steering increases keyword frequency; for word exclusion, subtracting the inclusion vector reduces undesired keyword occurrence. Multi-instruction steering (format+length, casing+word exclusion) improves both constraints simultaneously. Quality scores decrease slightly under steering, comparable to the decrease from simply adding instructions as text.
Evidence
interventional
Key metric
accuracy without instructions: ~10% (no steering) to ~30% (steering); accuracy with instructions: 60%-90% (no steering), significant boost for 2 of 4 models; length: c=20, significant improvement across n=1..5 sentences (p<0.01, McNemar's test)
Caveat
Steering can occasionally produce nonsensical or repetitive tokens, particularly for 'constrained response' and 'title' instructions where no layer improves performance. Quality score decreases are small but non-zero. The method uses a fixed steering weight throughout generation and does not fully explore the intervention space.