This was the main question and it took up most of the interview.
Start by clarifying the goal: to measure model robustness against universal adversarial prompts in a safe, offline setting using GPT-2. Then outline a framework that includes threat modeling, controlled prompt generation, evaluation metrics, and safeguards to prevent misuse.
Pro tip: Emphasize that the framework is for defensive research only, and propose using synthetic or benign prompts to avoid generating harmful content. This shows ethical awareness and aligns with Scale AI's responsible AI principles.
Clarify what 'universal adversarial prompts' means in this context and specify the model's intended use. Define success criteria for robustness, such as maintaining performance on a benchmark under attack.
Use offline, controlled methods to generate adversarial prompts, such as gradient-based optimization on a small set of benign inputs. Ensure prompts are not harmful and are stored securely.
Select metrics like attack success rate, model accuracy drop, and semantic similarity to measure robustness. Use a held-out test set to avoid overfitting.
Include access controls, logging, and review processes to prevent misuse. Limit the framework to offline use and ensure no real-world deployment of adversarial prompts.
Run experiments, analyze results, and refine the framework. Validate that the evaluation is reproducible and does not introduce new vulnerabilities.
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Acknowledge the constraints of black-box access and contrast them with white-box methods. Then outline a systematic approach that relies on input-output behavior, statistical testing, and surrogate modeling to evaluate the model's performance, robustness, and fairness. Emphasize creativity and rigor in designing experiments that extract meaningful signals without internal access.
Pro tip: Highlight that black-box evaluation often mirrors real-world deployment constraints, so demonstrating adaptability and resourcefulness here signals strong engineering maturity. Mention that combining multiple black-box techniques (e.g., behavioral testing, surrogate models) can approximate white-box insights.
Clarify what aspects of the model need evaluation (e.g., accuracy, robustness, bias) and acknowledge the limitations of black-box access. Prioritize which metrics are feasible and most critical.
Create targeted test cases, including edge cases and adversarial examples, to probe model behavior. Use statistical methods to analyze output distributions and detect anomalies.
Train a local surrogate model on input-output pairs to approximate the black-box model's decision boundaries. Use the surrogate to gain insights into feature importance and potential vulnerabilities.
Compare the model's outputs against baselines or across different demographic groups to identify performance disparities. Use techniques like A/B testing to isolate effects.
Refine experiments based on initial results, and validate hypotheses with additional tests. Document limitations and suggest white-box evaluations if possible.
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Talked about held-out prompt splits, diversity in the prompt distribution, and measuring transferability across prompt types.
Start by defining the universal adversarial string and the overfitting risk, then outline a systematic approach that includes diverse data, regularization, and rigorous evaluation. Emphasize the importance of separating training and validation prompts and using cross-validation to detect overfitting.
Pro tip: Mention that you would monitor the string's performance on a held-out set of prompts from different distributions and consider using ensemble methods to reduce variance. This shows practical experience with adversarial robustness.
Ensure the training prompt set is large and diverse, covering various topics, styles, and lengths to prevent the adversarial string from memorizing specific patterns.
Apply regularization techniques such as dropout, weight decay, or early stopping during the adversarial string optimization to discourage overfitting to the training set.
Split the prompt set into training and validation subsets, and evaluate the adversarial string's effectiveness on the validation set to detect overfitting.
Perform k-fold cross-validation or test on prompts from different distributions to assess generalization and adjust the string if overfitting is observed.
Consider generating multiple adversarial strings and ensembling them, or using techniques like adversarial training to improve robustness and reduce overfitting.
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Two-part question and I kind of rushed the second half.
Start by framing robustness evaluation as a multi-dimensional problem that requires diverse baselines to avoid misleading conclusions. Then, discuss the importance of withholding certain artifacts to prevent gaming and maintain evaluation integrity, while balancing transparency. Emphasize that the choice of baselines and withheld artifacts depends on the threat model and deployment context.
Pro tip: Mention that you would withhold the exact test set and any hyperparameters tuned on it, but share the evaluation protocol and baseline results to enable reproducibility without enabling overfitting.
Clarify what kinds of perturbations, distribution shifts, or adversarial attacks the robustness evaluation aims to cover. This determines which baselines are relevant and what artifacts might be sensitive.
Include a mix of baselines: a naive/random baseline, a standard non-robust model, a state-of-the-art robust model, and possibly human performance. This provides a spectrum of performance and highlights the gap between robust and non-robust methods.
Withhold artifacts that could lead to overfitting or gaming, such as the exact test set, any hyperparameters tuned on the test set, and potentially the model weights if they could be reverse-engineered. Share enough to allow reproducibility without compromising future evaluations.
Explain why certain artifacts are withheld and how the chosen baselines ensure a credible evaluation. This builds trust and shows awareness of the trade-offs between transparency and robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.