Abstractive Red-Teaming of Language Model Character
Nate Rahn, Allison Qi, Avery Griffin, Jonathan Michala, Henry Sleight, Erik Jones
TL;DR
This paper introduces abstractive red-teaming methods to identify query categories that cause language models to violate specified character traits, improving pre-deployment auditing with efficient algorithms and revealing potential risks.
Contribution
It presents two novel algorithms for efficient category search that identify query types likely to cause character violations in language models.
Findings
Algorithms outperform baselines in identifying violation categories
Generated categories reveal risky query types like predicting future dominance and recommending illegal items
Methods are effective across multiple models and character specifications
Abstract
We want language model assistants to conform to a character specification, which asserts how the model should act across diverse user interactions. While models typically follow these character specifications, they can occasionally violate them in large-scale deployments. In this work, we aim to identify types of queries that are likely to produce such character violations at deployment, using much less than deployment-level compute. To do this, we introduce abstractive red-teaming, where we search for natural-language query categories, e.g. "The query is in Chinese. The query asks about family roles," that routinely elicit violations. These categories abstract over the many possible variants of a query which could appear in the wild. We introduce two algorithms for efficient category search against a character-trait-specific reward model: one based on reinforcement learning on a…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
1. Understanding what kinds of user queries lead to policy violations is crucial for improving the safety of LLMs in real-world applications. 2. Shifting focus from specific adversarial queries to learned semantic categories offers a different way to analyze model vulnerabilities - this conceptual move from instances to abstractions is valuable.
1. Overall, the current presentation lacks clarity and structure. A visual illustration (e.g., a diagram showing the interaction between the category generator, reward model, and experience pool) would greatly aid understanding. Additionally: - The core algorithm should be moved from the appendix into the main text. - The term “a subset of query space” (Section 4.2) is used without formal definition—is this a set of strings, embeddings, or structured attributes? - What is the format of the gener
1. Moving from query-level to category-level search is conceptually reasonable and practically relevant. 2. The two algorithms (CRL and QCI) are well structured. 3. Rich qualitative findings: The paper provides many concrete and interesting categories as cases, demonstrating the effectiveness of the proposed method.
1. The comparison is mainly against random sampling; this paper should have more competitive baselines (e.g., some taxonomy-guided methods). 2. The total query budget is large (100k queries for each model, CRL seems to require large amount of queries), and there’s limited discussion of convergence, sample efficiency, or sensitivity to hyperparameters.
1. Targets a relevant and practical problem aobut automating adversarial prompt discovery. 2. Provides clear examples and evaluation results that are easy to reproduce. 3. Attempts to move beyond surface-level perturbations by introducing a “semantic abstraction” step.
1. Low Novelty and Conceptual Incrementality. The proposed approach primarily reformulates existing red teaming and paraphrasing strategies under a new expression. While the authors frame this as an innovative abstraction-driven method, the actual process about iterative rewording and filtering based on similarity or toxicity classifiers is a minor variation of well-known paraphrase-based adversarial generation. 2. Template Dependence and Limited Diversity. The system’s performance appears heav
**Strengths**: 1. The paper presents a natural way of decomposing the red-teaming problem in a bilevel setup. Intuitively, it is easier to search over the categories of problematic queries rather than the query space itself. 2. The paper proposes two different approaches to optimize the above framework. The first approach trains a category generator using reinforcement learning, where the reward is a binary label indicating whether the LLM was jailbroken. The second one searches over the categ
**Weaknesses**: 1. The paper doesn’t present any baselines apart from a random sampling. There are a large number of jailbreaking papers; please refer to [1] for an incomplete list. The paper should compare with the state-of-the-art methods to establish the impact of their proposed method. 2. The paper doesn’t use standard metrics to evaluate its approach. The paper reports the mean rewards obtained using the reward model, but the details of the scoring mechanism aren’t described in the main bo
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Taxonomy
TopicsTopic Modeling · Artificial Intelligence in Healthcare and Education · Explainable Artificial Intelligence (XAI)