Adversarial Pragmatics for AI Safety Evaluation: A Benchmark for Instruction Conflict, Embedded Commands, and Policy Ambiguity
Brett Reynolds
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Key claim
New framework improves safety evaluations of language models.
In plain English
Imagine you're building a language model that needs to follow complex instructions, like a virtual assistant that handles everything from scheduling to answering tricky questions. The challenge is that language is often ambiguous, and models can misinterpret instructions or fail to comply with policies. For instance, a model might not know whether to refuse a request or comply, leading to safety risks or incorrect outputs. This is what's called instruction conflict and ambiguity in language processing.
Currently, many evaluations reduce these complex behaviors to simple pass/fail labels, which can hide the real reasons behind a model's failures. When a model fails, it could be due to a lack of capability, unclear policies, or even the way evaluators interpret the model's responses. This oversimplification can lead to misunderstandings about a model's true performance and safety.
The paper introduces a new way to evaluate these behaviors by creating a detailed framework called adversarial pragmatics. This framework includes a controlled taxonomy of linguistic scenarios and a benchmark that assesses how well models handle various complexities, like indirect commands and ambiguous language. It also involves expert evaluations to distinguish between different types of task success and safety risks. By providing a more nuanced understanding of model behavior, this approach helps ensure that language models are safer and more reliable in real-world applications.
Compared to previous work, this framework allows for a deeper analysis of model performance, which is essential for building trustworthy AI systems. It means that when you're deploying language models, you can have a clearer picture of their strengths and weaknesses, leading to better safety documentation and more effective prompt-injection tests.
The paper introduces a new benchmark and methodology for evaluating language model behavior in complex linguistic scenarios.
The empirical approach includes a controlled taxonomy and expert evaluation, providing solid validation for the claims made.
Deep reliability assessment
The methodology supports a nuanced evaluation of language models under complex linguistic scenarios, but the paper may overclaim its ability to fully diagnose all safety-related failures without ambiguity. The framework's reliance on linguistic judgment could introduce subjectivity that isn't fully addressed.
Reproducibility
No open source code or dataset is mentioned in the paper, making reproducibility challenging.
Key figure
Figure 1 likely illustrates the taxonomy of adversarial pragmatics, showing how different linguistic elements are categorized for safety evaluation.