ContextBench: Modifying Contexts for Targeted Latent Activation and Behaviour Elicitation

Abstract

Identifying inputs that trigger specific behaviours or latent features in language models could have a wide range of safety use cases. We investigate a class of methods capable of generating targeted, linguistically fluent inputs that activate specific latent features or elicit model behaviours. We formalise this approach as *context modification* and present ContextBench - a benchmark with tasks designed to assess the capabilities of context modification methods across core capabilities and potential safety applications. Our evaluation framework measures both elicitation strength (the degree to which latent features or behaviours are successfully elicited) and linguistic fluency, highlighting how current state-of-the-art methods struggle to balance these objectives. We develop two novel enhancements to Evolutionary Prompt Optimisation, a gradient-based token-editing method: LLM-assistance and diffusion model inpainting, achieving strong performance in balancing elicitation and fluency. We release our benchmark here: https://github.com/lasr-eliciting-contexts/ContextBench.