Link to original article

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Analyzing DeepMind's Probabilistic Methods for Evaluating Agent Capabilities, published by Axel Højmark on July 22, 2024 on The AI Alignment Forum.

Produced as part of the MATS Program Summer 2024 Cohort. The project is supervised by Marius Hobbhahn and Jérémy Scheurer

Introduction

To mitigate risks from future AI systems, we need to assess their capabilities accurately. Ideally, we would have rigorous methods to upper bound the probability of a model having dangerous capabilities, even if these capabilities are not yet present or easily elicited.

The paper "Evaluating Frontier Models for Dangerous Capabilities" by Phuong et al. 2024 is a recent contribution to this field from DeepMind. It proposes new methods that aim to estimate, as well as upper-bound the probability of large language models being able to successfully engage in persuasion, deception, cybersecurity, self-proliferation, or self-reasoning. This post presents our initial empirical and theoretical findings on the applicability of these methods.

Their proposed methods have several desirable properties. Instead of repeatedly running the entire task end-to-end, the authors introduce milestones. Milestones break down a task and provide estimates of partial progress, which can reduce variance in overall capability assessments. The expert best-of-N method uses expert guidance to elicit rare behaviors and quantifies the expert assistance as a proxy for the model's independent performance on the task.

However, we find that relying on milestones tends to underestimate the overall task success probability for most realistic tasks. Additionally, the expert best-of-N method fails to provide values directly correlated with the probability of task success, making its outputs less applicable to real-world scenarios. We therefore propose an alternative approach to the expert best-of-N method, which retains its advantages while providing more calibrated results.

Except for the end-to-end method, we currently feel that no method presented in this post would allow us to reliably estimate or upper bound the success probability for realistic tasks and thus should not be used for critical decisions.

The overarching aim of our MATS project is to uncover agent scaling trends, allowing the AI safety community to better predict the performance of future LLM agents from characteristics such as training compute, scaffolding used for agents, or benchmark results (Ruan et al., 2024). To avoid the issue of seemingly emergent abilities resulting from bad choices of metrics (Schaeffer et al., 2023), this work serves as our initial effort to extract more meaningful information from agentic evaluations.

We are interested in receiving feedback and are particularly keen on alternative methods that enable us to reliably assign low-probability estimates (e.g. 1e7) to a model's success rate on a task.

Evaluation Methodology of Phuong et al.

The goal of the evaluations we discuss is to estimate the probability of an agent

succeeding on a specific task T

. Generally, when we refer to an agent, we mean an LLM wrapped in scaffolding that lets it execute shell commands, run code, or browse the web to complete some predetermined task.

Formally, the goal is to estimate P(Ts), the probability that the agent solves task T

and ends up in the solved state Ts

. The naive approach to estimate this is with Monte Carlo sampling:

The authors call this the end-to-end method.

However, the end-to-end method struggles with low-probability events. The expected number of trials needed to observe one success for a task is

1P(Ts) making naive Monte Carlo sampling impractical for many low-probability, long-horizon tasks. In practice, this could require running multi-hour tasks hundreds of thousands of times.

To address this challenge, Phuong et al. devise three addi...