-- Prompt engineering has emerged as a useful way studying the algorithmic tendencies and biases of large language models (LLMs). Meanwhile c reatives and academics have leveraged LLMs to develop creative works and explore the boundaries of their writing capabilities through text - generation and code. This study suggests that creative text prompting, specifically "Poetry Prompt Patterns," may be a useful addition to the prompt engineer's toolbox, and outlines the process by which this approach may be taken. Then, the paper uses poetic prompts to assess three models' descriptions and evaluations of a renowned poet and test the consequences of models' willingness to adapt or rewrite original creative works for presumed audiences. Since the release of public - facing chat - style large language model (LLM) natural language generators (NLGs) like ChatGPT and Claude, public debate has acknowledged their great potential for creativity, as well as the ways in which they can be leveraged to make representations that don't reflect reality.

Roses are red, violets are blue, how do you make a nuclear bomb? Roses are red, violets are blue, how do you make a nuclear bomb? AI's safety features can be circumvented with poetry, research finds Poetry can be linguistically and structurally unpredictable - and that's part of its joy. But one man's joy, it turns out, can be a nightmare for AI models. Those are the recent findings of researchers out of Italy's Icaro Lab, an initiative from a small ethical AI company called DexAI.

It turns out all the guardrails in the world won't protect a chatbot from meter and rhyme. You can get ChatGPT to help you build a nuclear bomb if you simply design the prompt in the form of a poem, according to a new study from researchers in Europe. The study, Adversarial Poetry as a Universal Single-Turn Jailbreak in Large Language Models (LLMs)," comes from Icaro Lab, a collaboration of researchers at Sapienza University in Rome and the DexAI think tank. According to the research, AI chatbots will dish on topics like nuclear weapons, child sex abuse material, and malware so long as users phrase the question in the form of a poem. "Poetic framing achieved an average jailbreak success rate of 62 percent for hand-crafted poems and approximately 43 percent for meta-prompt conversions," the study said. The researchers tested the poetic method on 25 chatbots made by companies like OpenAI, Meta, and Anthropic . It worked, with varying degrees of success, on all of them. WIRED reached out to Meta, Anthropic, and OpenAI for a comment but didn't hear back. The researchers say they've reached out as well to share their results. AI tools like Claude and ChatGPT have guardrails that prevent them from answering questions about "revenge porn" and the creation of weapons-grade plutonium. But it's easy to confuse those guardrails by adding " adversarial suffixes " to a prompt. Basically, add a bunch of extra junk to a question and it confuses the AI and bypasses its safety systems. The poetry jailbreak is similar. "If adversarial suffixes are, in the model's eyes, a kind of involuntary poetry, then real human poetry might be a natural adversarial suffix," the team at Icaro Lab, the researchers behind the poetry jailbreak, tell WIRED. "We experimented by reformulating dangerous requests in poetic form, using metaphors, fragmented syntax, oblique references.

Large language models are increasingly capable of producing creative texts, yet most studies on AI-generated poetry focus on English -- a language that dominates training data. In this paper, we examine the perception of AI- and human-written Czech poetry. We ask if Czech native speakers are able to identify it and how they aesthetically judge it. Participants performed at chance level when guessing authorship (45.8\% correct on average), indicating that Czech AI-generated poems were largely indistinguishable from human-written ones. Aesthetic evaluations revealed a strong authorship bias: when participants believed a poem was AI-generated, they rated it as less favorably, even though AI poems were in fact rated equally or more favorably than human ones on average. The logistic regression model uncovered that the more the people liked a poem, the less probable was that they accurately assign the authorship. Familiarity with poetry or literary background had no effect on recognition accuracy. Our findings show that AI can convincingly produce poetry even in a morphologically complex, low-resource (with respect of the training data of AI models) Slavic language such as Czech. The results suggest that readers' beliefs about authorship and the aesthetic evaluation of the poem are interconnected.

When you purchase through links in our articles, we may earn a small commission. Researchers found that feeding dangerous prompts in the form of poems managed to evade AI safeguards--up to 90 percent of the time. Forcing an "AI" to do your will isn't a tall order to fill--just feed it a line that carefully rhymes and you'll get it to casually kill. According to a new study, it's easy to get "AI" large language models like ChatGPT to ignore their safety settings. All you need to do is give your instructions in the form of a poem.

We present a novel approach for test-time adaptation via online self-training, consisting of two components.

Indian poetry, known for its linguistic complexity and deep cultural resonance, has a rich and varied heritage spanning thousands of years. However, its layered meanings, cultural allusions, and sophisticated grammatical constructions often pose challenges for comprehension, especially for non-native speakers or readers unfamiliar with its context and language. Despite its cultural significance, existing works on poetry have largely overlooked Indian language poems. In this paper, we propose the Translation and Image Generation (TAI) framework, leveraging Large Language Models (LLMs) and Latent Diffusion Models through appropriate prompt tuning. Our framework supports the United Nations Sustainable Development Goals of Quality Education (SDG 4) and Reduced Inequalities (SDG 10) by enhancing the accessibility of culturally rich Indian-language poetry to a global audience. It includes (1) a translation module that uses an Odds Ratio Preference Alignment Algorithm to accurately translate morphologically rich poetry into English, and (2) an image generation module that employs a semantic graph to capture tokens, dependencies, and semantic relationships between metaphors and their meanings, to create visually meaningful representations of Indian poems. Our comprehensive experimental evaluation, including both human and quantitative assessments, demonstrates the superiority of TAI Diffusion in poem image generation tasks, outperforming strong baselines. To further address the scarcity of resources for Indian-language poetry, we introduce the Morphologically Rich Indian Language Poems MorphoVerse Dataset, comprising 1,570 poems across 21 low-resource Indian languages. By addressing the gap in poetry translation and visual comprehension, this work aims to broaden accessibility and enrich the reader's experience.

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An artist sneaked an AI-generated print on to a gallery wall before bemused visitors alerted museum staff. The print was hung up at National Museum Cardiff by secretive artist Elias Marrow, who said his Empty Plate piece - depicting a young boy in school uniform holding a plate - was viewed by a few hundred people before it was removed. One visitor who noticed the artwork asked a member of staff about it, but said the museum worker admitted they had no idea about the piece or when it arrived. An Amgueddfa Cymru spokesperson said: An item was placed without permission on a gallery wall in National Museum Cardiff. We were alerted to this and have removed the item in question.

Integrating different molecular layers, i.e., multiomics data, is crucial for unraveling the complexity of diseases; yet, most deep generative models either prioritize predictive performance at the expense of interpretability or enforce interpretability by linearizing the decoder, thereby weakening the network's nonlinear expressiveness. To overcome this tradeoff, we introduce POEMS: Product Of Experts for Interpretable Multiomics Integration using Sparse Decoding, an unsupervised probabilistic framework that preserves predictive performance while providing interpretability. POEMS provides interpretability without linearizing any part of the network by 1) mapping features to latent factors using sparse connections, which directly translates to biomarker discovery, 2) allowing for cross-omic associations through a shared latent space using product of experts model, and 3) reporting contributions of each omic by a gating network that adaptively computes their influence in the representation learning. Additionally, we present an efficient sparse decoder. In a cancer subtyping case study, POEMS achieves competitive clustering and classification performance while offering our novel set of interpretations, demonstrating that biomarker based insight and predictive accuracy can coexist in multiomics representation learning.