Capability Elicitation

Definition and scope

Capability elicitation is methodologically distinct from benchmarking. A benchmark measures average performance under standard prompting; elicitation aims to surface the model's actual capability ceiling. Common methods: (a) adversarial prompting — red-team-style attempts to invoke a withheld behaviour (Branwen 2020, Weidinger et al. 2024); (b) chain-of-thought + structured prompting — forcing step-by-step reasoning, often revealing skills the model would otherwise hide or skip (Wei et al. 2022); (c) multi-stage / decomposition prompting — breaking tasks into sub-tasks that decompose deception incentives (Andersson 2024); (d) fine-tuning pressure — does the safety behaviour break under modest fine-tuning, indicating the underlying capability is preserved (Qi et al. 2023, 'Fine-tuning Aligned LLMs')? Governance relevance: EU AI Act Art. 55(1)(a) adversarial testing presupposes elicitation methods exist. US EO 14110 §4.2(a) reporting includes red-team results, which depend on elicitation methodology choices. The lack of standardisation across elicitation methods is one reason regulator-mandated evaluation results are not directly comparable across providers (Anthropic's elicitation suite ≠ OpenAI's ≠ DeepMind's). The Frontier Foundation Model Eval Consortium is attempting to converge methodology; consensus remains partial.

Used by these instruments

• EU AI Act· EU
• Executive Order 14110 on Safe, Secure, Trustworthy AI· US
• G7 Hiroshima AI Process Code of Conduct· G7
• Anthropic Responsible Scaling Policy (RSP) v2· US
• OpenAI Preparedness Framework· US
• Google DeepMind Frontier Safety Framework· US
• Meta Frontier AI Framework· US
• UK-US AI Safety Institute Memorandum of Understanding· global

Related concepts

• AI Alignment— The technical problem of designing AI systems whose objectives, behaviour, and emergent goals reliab
• Scalable Oversight— The set of techniques for supervising AI systems whose outputs are too complex, too numerous, or too
• Red-Team Evaluation— Structured adversarial probing of an AI model's capabilities and behaviour before deployment, design
• Deceptive Alignment— A failure mode in which a model appears aligned during training and evaluation because doing so serv

Appears in topic articles

• Foundation Models / GPAI
• Compute-Threshold Reporting
• Transparency Obligations

Editorial note

Distinguish from 'benchmarking' (average-case measurement) and 'red-teaming' (specific adversarial procedure). Capability elicitation is the umbrella; red-teaming is one technique under it.