Francis Rhys Ward

We are releasing a new paper called “The Elicitation Game: Evaluating Capability Elicitation Techniques”. See tweet thread here. TL;DR: We train LLMs to only reveal their capabilities when given a password. We then test methods for eliciting the LLMs capabilities without the password. Fine-tuning works best, few-shot prompting and prefilling...

In this new paper, I discuss what it would mean for AI systems to be persons — entities with properties like agency, theory-of-mind, and self-awareness — and why this is important for alignment. In this post, I say a little more about why you should care. The existential safety literature...

We have written a paper on sandbagging for which we present the abstract and brief results in this post. See the paper for more details. Tweet thread here. Illustration of sandbagging. Evaluators may regulate the deployment of AI systems with dangerous capabilities, potentially against the interests of the AI system...

Summary: Evaluations provide crucial information to determine the safety of AI systems which might be deployed or (further) developed. These development and deployment decisions have important safety consequences, and therefore they require trustworthy information. One reason why evaluation results might be untrustworthy is sandbagging, which we define as strategic underperformance...

Produced as part of the ML Alignment Theory Scholars Program Winter 2024 Cohort, under the mentorship of Francis Rhys Ward. The code, data, and plots can be found on https://github.com/TeunvdWeij/MATS/tree/main/distribution_approximation. This post is meant to provide insight on an interesting LLM capability, which is useful for targeted underperformance on evaluations...

This post summarizes work done over the summer as part of the Summer 2023 AI Safety Hub Labs programme. Our results will also be published as part of an upcoming paper. In this post, we focus on explaining how we define and evaluate properties of deceptive behavior in LMs and...

Post 4 of Towards Causal Foundations of Safe AGI, preceded by Post 1: Introduction, Post 2: Causality, Post 3: Agency, and Post 4: Incentives. By Francis Rhys Ward, Tom Everitt, Sebastian Benthall, James Fox, Matt MacDermott, Milad Kazemi, Ryan Carey representing the Causal Incentives Working Group. Thanks also to Toby...