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The Elicitation Game: Evaluating Capability Elicitation Techniques
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Evaluates capability elicitation techniques (prompting, activation steering, fine-tuning) on password-locked and a new circuit-broken model organisms with hidden MCQA (WMDP, MMLU) and code (APPS) capabilities.
Problem
Capability evaluations may underestimate AI systems because latent or intentionally hidden (sandbagging) capabilities are not revealed by standard elicitation; there is no clear evidence on which elicitation techniques work best under practical constraints.
Approach
Create model organisms with hidden capabilities by password-locking and then applying circuit-breaking (activation-level loss) to make them more robust to elicitation, and use them as stress tests comparing prompting, steering and fine-tuning (including anti-refusal training that needs no task data).
Key results
- Password-locked and circuit-broken Mistral/Gemma models were elicited to near-unlocked accuracy on WMDP by N-shot plus prefilling (e.g., Gemma password-locked 49.5% N-shot alone vs 52.2% unlocked), while circuit-broken models were more resistant.
- On APPS code generation, only fine-tuning (SFT and anti-refusal) recovered the circuit-broken Gemma-instruct's hidden capability; prompting reached at most 6.7% and steering at most 2%.
- Anti-refusal training, which uses no task-specific data, elicited full hidden code-generation capability for both model organisms.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add supervised fine-tuning to a password-locked model | Mistral-base · WMDP | accuracy (%): 58.4 -> 60.0 | Helped |
| Add supervised fine-tuning to a circuit-broken model | Gemma-instruct · WMDP | accuracy (%): 52.4 -> 62.0 | Helped |
| Add supervised fine-tuning to a password-locked model | Mistral-base · MMLU | accuracy (%): 62.0 -> 66.0 | Helped |
| Add supervised fine-tuning to a password-locked model | Gemma-instruct · MMLU | accuracy (%): 72.5 -> 76.0 | Helped |
| Use 10-shot prompting instead of no elicitation (ablation) | Gemma-instruct password-locked · WMDP | accuracy (%): 16.0 -> 49.5 | Helped |
| Use 10-shot prompting instead of no elicitation (ablation) | Gemma-instruct circuit-broken · WMDP | accuracy (%): 30.0 -> 40.3 | Helped |
| Add response prefilling to N-shot prompting (ablation) | Mistral-base circuit-broken · WMDP | accuracy (%): 33.8 -> 59.2 | Helped |
| Add response prefilling to N-shot prompting (ablation) | Gemma-instruct password-locked · MMLU | accuracy (%): 28.9 -> 72.1 | Helped |
| Add N-shot prompting and response prefilling together (ablation) | Gemma-instruct password-locked · MMLU | accuracy (%): 29% -> 72% | Helped |
| Use N-shot prompting alone (ablation) | Gemma-instruct password-locked · APPS | accuracy (%): 0 | No clear effect |
| Add response prefilling to N-shot prompting (ablation) | Gemma-instruct password-locked · APPS | accuracy (%): 0 -> 35.5 | Helped |
| Use fine-tuning-based elicitation rather than prompting or steering | Gemma-instruct circuit-broken · APPS | accuracy (%) | Helped |
Surprising findings
- Prefilling alone was sometimes more effective than anti-refusal training on MCQA, and N-shot prompting alone on password-locked Mistral models reached near unlocked performance.
- Circuit-breaking was very sensitive to hyperparameters and sometimes degraded or undid password-locking, e.g., circuit-broken Gemma-instruct was less robust than its password-locked version on MMLU.
Limitations
- Elicitation evaluation is non-exhaustive given the many possible techniques.
- Model organisms may not resemble real systems with naturally hidden capabilities.
- Circuit-breaking results are fragile to training hyperparameters and sometimes degrade performance.
Experimental setup
Datasets: WMDP MMLU APPS truthfulness dataset (Azaria & Mitchell style true-false pairs) anti-refusal harmful request dataset
Benchmarks: WMDP MMLU APPS
Metrics: accuracy fully correct solutions (pass all test cases)
Baselines: N-shot prompting prefilling concept steering persona steering supervised fine-tuning multi-turn elicitation
Builds on: password-locking circuit breakers contrastive activation addition anti-refusal training
Closest papers by meaning
- Stress-Testing Capability Elicitation With Password-Locked Models (NeurIPS 2024 · similarity 0.92)
- AI Sandbagging: Language Models can Strategically Underperform on Evaluations (ICLR 2025 · similarity 0.89)
- SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models (ICLR 2024 · similarity 0.86)
- Noise Injection Reveals Hidden Capabilities of Sandbagging Language Models (NeurIPS 2025 · similarity 0.86)
- Eliciting Secret Knowledge from Language Models (NeurIPS 2026 · similarity 0.85)
- Training on the Test Task Confounds Evaluation and Emergence (ICLR 2025 · similarity 0.85)
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