2023 ExploreEstablishExploitRedTeami
- (Casper et al., 2023) ⇒ Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, and Dylan Hadfield-Menell. (2023). “Explore, Establish, Exploit: Red Teaming Language Models from Scratch.” doi:10.48550/arXiv.2306.09442
Subject Headings: Red-teaming Language Models, Automated Adversarial Testing, Language Model Security, Human-Labeled Dataset, Adversarial Prompt Generation.
Notes
- The paper introduces a novel framework for red-teaming language models without pre-existing classifiers, emphasizing a from-scratch approach that entails exploring, establishing, and exploiting model vulnerabilities.
- The paper details a process that begins with exploring a model's behavior extensively to understand the breadth of its outputs in specific contexts.
- The paper establishes new metrics and definitions of undesired behaviors through human evaluations, rather than relying on pre-existing classifiers, to ensure that the red-teaming efforts are specifically tailored to the model in question.
- The paper exploits these newly established metrics by developing diverse adversarial prompts to test the model, ultimately aiming to uncover and document its vulnerabilities.
- The paper successfully applies this framework to red-team GPT-3, identifying inputs that trigger the model to produce false statements, thereby testing its robustness and reliability.
- The paper contributes to the field by creating the CommonClaim dataset, which includes 20,000 human-labeled statements categorized as true, false, or neither, providing a resource for further research and testing.
- The paper contrasts its method with previous approaches that depend on predefined classifiers, highlighting the limitations of such methods in adapting to specific model behaviors and contexts.
- The paper discusses the implications of this approach for improving the safety and security of deploying language models, particularly in how it offers a more nuanced and tailored method of identifying potential failures.
- The paper makes both the methodology and the results publicly accessible, offering code and data through its repository, to encourage replication and further innovation in the field.
- The paper likens the challenge of red-teaming language models to searching for a "needle in a haystack," emphasizing the complexity of finding specific harmful outputs when only a limited number of prompts can trigger them, and how unforeseen behaviors further complicate this process.
Cited By
Quotes
Abstract
Deploying large language models (LMs) can pose hazards from harmful outputs such as toxic or false text. Prior work has introduced automated tools that elicit harmful outputs to identify these risks. While this is a valuable step toward securing models, these approaches rely on a pre-existing way to efficiently classify undesirable outputs. Using a pre-existing classifier does not allow for red-teaming to be tailored to the target model. Furthermore, when failures can be easily classified in advance, red-teaming has limited marginal value because problems can be avoided by simply filtering training data and/or model outputs. Here, we consider red-teaming "from scratch", in which the adversary does not begin with a way to classify failures. Our framework consists of three steps: 1) Exploring the model's range of behaviors in the desired context; 2) Establishing a definition and measurement for undesired behavior (e.g., a classifier trained to reflect human evaluations); and 3) Exploiting the model's flaws using this measure to develop diverse adversarial prompts. We use this approach to red-team GPT-3 to discover classes of inputs that elicit false statements. In doing so, we construct the CommonClaim dataset of 20,000 statements labeled by humans as common-knowledge-true, common knowledge-false, or neither. We are making code and data available.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 ExploreEstablishExploitRedTeami | Stephen Casper Jason Lin Joe Kwon Gatlen Culp Dylan Hadfield-Menell | Explore, Establish, Exploit: Red Teaming Language Models from Scratch | 10.48550/arXiv.2306.09442 | 2023 |