Evaluating the Goal-Directedness of Large Language Models

• URL: http://arxiv.org/abs/2504.11844v1
• Date: Wed, 16 Apr 2025 08:07:08 GMT
• Title: Evaluating the Goal-Directedness of Large Language Models
• Authors: Tom Everitt, Cristina Garbacea, Alexis Bellot, Jonathan Richens, Henry Papadatos, Siméon Campos, Rohin Shah,
• Abstract summary: We evaluate goal-directedness on tasks that require information gathering, cognitive effort, and plan execution.<n>Our evaluations of LLMs from Google DeepMind, OpenAI, and Anthropic show that goal-directedness is relatively consistent across tasks.
• Score: 17.08087240111954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To what extent do LLMs use their capabilities towards their given goal? We take this as a measure of their goal-directedness. We evaluate goal-directedness on tasks that require information gathering, cognitive effort, and plan execution, where we use subtasks to infer each model's relevant capabilities. Our evaluations of LLMs from Google DeepMind, OpenAI, and Anthropic show that goal-directedness is relatively consistent across tasks, differs from task performance, and is only moderately sensitive to motivational prompts. Notably, most models are not fully goal-directed. We hope our goal-directedness evaluations will enable better monitoring of LLM progress, and enable more deliberate design choices of agentic properties in LLMs.

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