Beyond Multiple Choice: Evaluating Steering Vectors for Adaptive Free-Form Summarization

Joschka Braun, Carsten Eickhoff, Seyed Ali Bahrainian

Keywords: Steering Vectors, Activation Engineering, Controllable Text Generation, Free-Form Summarization, Adaptive Summarization, Language Model Control

TL;DR: Evaluate the efficacy-quality trade-off of steering vectors in adaptive free-form summarisation.

Abstract: Steering vectors are a lightweight method to control text properties by adding a learned bias to language model activations at inference time. So far, steering vectors have predominantly been evaluated in multiple-choice settings, while their effectiveness in free-form generation tasks remains understudied. Moving "Beyond Multiple Choice," we thoroughly evaluate the effectiveness of steering vectors in adaptively controlling topical focus, sentiment, toxicity, and readability in abstractive summaries of the NEWTS dataset. We find that steering effectively controls the targeted summary properties, but high steering strengths consistently degrade both intrinsic and extrinsic text quality. Compared to steering, prompting offers weaker control, while preserving text quality. Combining steering and prompting yields the strongest control over text properties and offers the most favorable efficacy-quality trade-off at moderate steering strengths. Our results underscore the practical trade-off between control strength and text quality preservation when applying steering vectors to free-form generation tasks.

Submission Number: 9