Various approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target.Several of them task attacker with formulating its own strategies to transform harmful queries into jailbreaks through trial and error, resulting in a semantically limited range of successful attacks.Another recent approach discovers diverse attacks by combining crowdsourced queries and tactics within the attacker's instructions, but does so at random, limiting effectiveness.This article introduces a novel framework, ADAPTIVE INSTRUCTION COM-POSITION, that combines crowdsourced texts according to an adaptive mechanism trained to jointly optimize attack effectiveness with diversity.We use reinforcement learning to balance exploration with exploitation in a combinatorial space of instructions to guide the attacker toward diverse generations tailored to target vulnerabilities.We show that our strategy substantially outperforms random combination on a set of effectiveness and diversity metrics, even under model transfer.Further, we show that it surpasses a host of recent adaptive approaches on the public benchmark Harmbench.We employ a lightweight neural contextual bandit that adapts to contrastively pretrained embeddings, and provide ablations to suggest that the contrastive property enables the network to generalize and scale to the massive space.Warning: this article discusses malicious content and methods for generating it using LLMs.

Multi-turn textual feedback-based fashion image retrieval focuses on a real-world setting, where users can iteratively provide information to refine retrieval results until they find an item that fits all their requirements. In this work, we present a novel memory-based method, called FashionNTM, for such a multi-turn system. Our framework incorporates a new Cascaded Memory Neural Turing Machine (CM-NTM) approach for implicit state management, thereby learning to integrate information across all past turns to retrieve new images, for a given turn. Unlike vanilla Neural Turing Machine (NTM), our CM-NTM operates on multiple inputs, which interact with their respective memories via individual read and write heads, to learn complex relationships. Extensive evaluation results show that our proposed method outperforms the previous state-of-the-art algorithm by 50.5%, on Multi-turn FashionIQ [60] – the only existing multi-turn fashion dataset currently, in addition to having a relative improvement of 12.6% on Multi-turn Shoes – an extension of the singleturn Shoes dataset [5] that we created in this work. Further analysis of the model in a real-world interactive setting demonstrates two important capabilities of our model – memory retention across turns, and agnosticity to turn order for non-contradictory feedback. Finally, user study results show that images retrieved by FashionNTM were favored by 83.1% over other multi-turn models.