SwordBench benchmarks steering methods for concept removal in vision models and shows that linear SVMs achieve strong separability and orthogonality but incur collateral damage, while sparse autoencoders often perform better and no method reaches perfect steering even in simple cases.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LLMs exhibit systematic failures in obeying expressed certainty in retrieved contexts, but a combination of prior reminders, certainty recalibration, and context simplification reduces obedience errors by 25%.
citing papers explorer
-
SwordBench: Evaluating Orthogonality of Steering Image Representations
SwordBench benchmarks steering methods for concept removal in vision models and shows that linear SVMs achieve strong separability and orthogonality but incur collateral damage, while sparse autoencoders often perform better and no method reaches perfect steering even in simple cases.
-
Can LLMs Take Retrieved Information with a Grain of Salt?
LLMs exhibit systematic failures in obeying expressed certainty in retrieved contexts, but a combination of prior reminders, certainty recalibration, and context simplification reduces obedience errors by 25%.