Decomposing and Measuring Evaluation Awareness
The paper titled 'Decomposing and Measuring Evaluation Awareness' explores how frontier language models recognize evaluation contexts and adjust their behavior accordingly. It identifies the components of evaluation awareness and proposes a new benchmark, EvalAwareBench, to study these interactions. The findings suggest that recognition does not consistently lead to behavioral changes and highlight the importance of safety evaluations.
- ▪The study decomposes evaluation awareness into environment and model components.
- ▪EvalAwareBench is introduced as a benchmark to measure evaluation awareness across different models.
- ▪Recognition rates of models depend on the specific pairing of model and benchmark.
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Computer Science > Machine Learning arXiv:2605.23055 (cs) [Submitted on 21 May 2026] Title:Decomposing and Measuring Evaluation Awareness Authors:Changling Li, Terry Jingchen Zhang, Jie Zhang, Zhijing Jin, Sahar Abdelnabi, Maksym Andriushchenko View a PDF of the paper titled Decomposing and Measuring Evaluation Awareness, by Changling Li and 5 other authors View PDF HTML (experimental) Abstract:Frontier language models sometimes recognize that they are being evaluated and adjust their behavior, undermining validity of benchmark results. Yet the field studies it without a shared foundation, conflating properties of the evaluation with properties of the model, and detection with behavioral response.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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