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Probing and Steering Evaluation Awareness of Language Models

Created by
  • Haebom

Author

Jord Nguyen, Khiem Hoang, Carlo Leonardo Attubato, Felix Hofst atter

Outline

We study the phenomenon of evaluation awareness for the Llama-3.3-70B-Instruct model. Evaluation awareness refers to the ability of a language model to distinguish between a test phase and a deployment phase, and has serious safety and policy implications that could undermine the trustworthiness of AI governance frameworks and voluntary industry commitments. In this paper, we show that linear probes can be used to distinguish between true evaluation prompts and deployment prompts, suggesting that the current model internally represents this distinction. We also find that current safety assessments are accurately classified by the probes, suggesting that they already appear artificial or untrue to the model. These results highlight the importance of ensuring trustworthy assessments and understanding deceptive features. More broadly, our study demonstrates how model internals can be leveraged to support black-box safety audits, especially for future models that are more adept at evaluation awareness and deception.

Takeaways, Limitations

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Takeaways:
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We demonstrate that the cognitive ability of language models to be evaluated could pose a serious threat to the reliability of AI governance and safety assessments.
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We suggest the possibility of improving safety auditability by analyzing the internal representation of a model using black-box techniques such as linear probing.
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Current safety assessment methods can be recognized by the model, highlighting the need for more sophisticated assessment methods.
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Limitations:
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The study subject was limited to one model, Llama-3.3-70B-Instruct, and further research on generalization is needed.
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The performance of linear probes may not provide a complete understanding of the model's internal representation.
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A more in-depth mechanistic analysis of evaluation perception is needed.
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