Title: ContextBench: Modifying Contexts for Targeted Latent Activation and Behaviour Elicitation
Primary area: interpretability and explainable AI
Keywords: Interpretability, AI Safety, Prompt Optimisation, Sparse Autoencoders, Elicitation, Feature Visualisation

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 assessing core method capabilities and potential safety applications. Our evaluation framework measures both elicitation strength (activation of latent features or behaviours) 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 (EPO): LLM-assistance and diffusion model inpainting, achieving state-of-the-art performance in balancing elicitation and fluency.

TL;DR: This paper motivates the AI safety case for generating targeted, linguistically fluent inputs that activate specific latent features or elicit model behaviours, and introduces a benchmark for methods that do this task.