Hasil untuk "Style. Composition. Rhetoric"

Julia Jose, Ritik Roongta, Rachel Greenstadt

Despite their wide-ranging benefits, LLM-based agents deployed in open environments can be exploited to produce manipulative material. In this study, we task LLMs with propaganda objectives and analyze their outputs using two domain-specific models: one that classifies text as propaganda or non-propaganda, and another that detects rhetorical techniques of propaganda (e.g., loaded language, appeals to fear, flag-waving, name-calling). Our findings show that, when prompted, LLMs exhibit propagandistic behaviors and use a variety of rhetorical techniques in doing so. We also explore mitigation via Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and ORPO (Odds Ratio Preference Optimization). We find that fine-tuning significantly reduces their tendency to generate such content, with ORPO proving most effective.

Daocheng Lin, Yifan Wang, Yutong Yang et al.

When designed deliberately, data visualizations can become powerful persuasive tools, influencing viewers' opinions, values, and actions. While researchers have begun studying this issue (e.g., to evaluate the effects of persuasive visualization), we argue that a fundamental mechanism of persuasion resides in rhetorical construction, a perspective inadequately addressed in current visualization research. To fill this gap, we present a focused analysis of octopus maps, a visual genre that has maintained persuasive power across centuries and achieved significant social impact. Employing rhetorical schema theory, we collected and analyzed 90 octopus maps spanning from the 19th century to contemporary times. We closely examined how octopus maps implement their persuasive intents and constructed a design space that reveals how visual metaphors are strategically constructed and what common rhetorical strategies are applied to components such as maps, octopus imagery, and text. Through the above analysis, we also uncover a set of interesting findings. For instance, contrary to the common perception that octopus maps are primarily a historical phenomenon, our research shows that they remain a lively design convention in today's digital age. Additionally, while most octopus maps stem from Western discourse that views the octopus as an evil symbol, some designs offer alternative interpretations, highlighting the dynamic nature of rhetoric across different sociocultural settings. Lastly, drawing from the lessons provided by octopus maps, we discuss the associated ethical concerns of persuasive visualization.

Houjiang Liu, Yiheng Su, Matthew Lease

We explore potential benefits of incorporating Rhetorical Design into the design of Explainable Artificial Intelligence (XAI) systems. While XAI is traditionally framed around explaining individual predictions or overall system behavior, explanations may also function as rhetorical arguments that shape how users evaluate a system's usefulness and credibility, and how they develop appropriate trust for adoption. In real-world, in-situ interactions, explanations can thus produce experiential and affective rhetorical effects that are not fully captured by traditional XAI design goals that focus primarily on how AI works. To address this gap, we propose Rhetorical XAI, which bridges two explanatory goals: how AI works and why AI merits use. Rhetorical XAI comprises three appeals in explanation design: logos, which aligns technical logic with human reasoning through visual and textual abstractions; ethos, which establishes contextual credibility based on the explanation source and its appropriateness to the decision task; and pathos, which engages user emotionally by framing explanations around their motivations, expectations, or situated needs during interaction. We conduct a narrative review synthesizing design strategies from prior XAI work aligned with these three rhetorical appeals, highlighting both opportunities and challenges of integrating rhetorical design into XAI.

Zitong Zhou, Ben Limpanukorn, Hong Jin Kang et al.

Ensuring the correctness of compiler optimizations is critical, but existing fuzzers struggle to test optimizations effectively. First, most fuzzers use optimization pipelines (heuristics-based, fixed sequences of passes) as their harness. The phase-ordering problem can enable or preempt transformations, so pipelines inevitably miss optimization interactions; moreover, many optimizations are not scheduled, even at aggressive levels. Second, optimizations typically fire only when inputs satisfy specific structural relationships, which existing generators and mutations struggle to produce. We propose targeted fuzzing of individual optimizations to complement pipeline-based testing. Our key idea is to exploit composition styles - structural relations over program constructs (adjacency, nesting, repetition, ordering) - that optimizations look for. We build a general-purpose, grammar-based mutational fuzzer, TargetFuzz, that (i) mines composition styles from an optimization-relevant corpus, then (ii) rebuilds them inside different contexts offered by a larger, generic corpus via synthesized mutations to test variations of optimization logic. TargetFuzz is adaptable to a new programming language by lightweight, grammar-based, construct annotations - and it automatically synthesizes mutators and crossovers to rebuild composition styles. No need for hand-coded generators or language-specific mutators, which is particularly useful for modular frameworks such as MLIR, whose dialect-based, rapidly evolving ecosystem makes optimizations difficult to fuzz. Our evaluation on LLVM and MLIR shows that TargetFuzz improves coverage by 8% and 11% and triggers optimizations 2.8$\times$ and 2.6$\times$, compared to baseline fuzzers under the targeted fuzzing mode. We show that targeted fuzzing is complementary: it effectively tests all 37 sampled LLVM optimizations, while pipeline-fuzzing missed 12.

Marc Angenot, Marianne Doury, Théophile Robineau

In the interview he gave to Marianne Doury and Théophile Robineau, Marc Angenot discusses the place of rhetoric in his work. Recalling that ancient rhetoric was essentially based on the judicial model, he emphasizes its interest, but also its limitations, in exploring the social discourse he seeks to account for. He takes up its global perspective, mobilizing ethos, pathos and logos, the combined consideration of which is necessary for an understanding of ideas and the way in which they are carried and discussed in society. But he insists on the necessity of taking account of the fact that social discourse is embedded in a more or less long-term history, which is a prerequisite for its intelligibility. Taking social discourse as the object of research also requires us to reconsider the notion of situation as traditionally understood in rhetoric, and to redefine the way we look at the question of persuasion.

Wyatt Scott, Brett Genz, Sarah Elmasry et al.

In political discourse and geopolitical analysis, national leaders words hold profound significance, often serving as harbingers of pivotal historical moments. From impassioned rallying cries to calls for caution, presidential speeches preceding major conflicts encapsulate the multifaceted dynamics of decision-making at the apex of governance. This project aims to use deep learning techniques to decode the subtle nuances and underlying patterns of US presidential rhetoric that may signal US involvement in major wars. While accurate classification is desirable, we seek to take a step further and identify discriminative features between the two classes (i.e. interpretable learning). Through an interdisciplinary fusion of machine learning and historical inquiry, we aspire to unearth insights into the predictive capacity of neural networks in discerning the preparatory rhetoric of US presidents preceding war. Indeed, as the venerable Prussian General and military theorist Carl von Clausewitz admonishes, War is not merely an act of policy but a true political instrument, a continuation of political intercourse carried on with other means (Clausewitz, 1832).

Zhen Tao, Dinghao Xi, Zhiyu Li et al.

Text style transfer plays a vital role in online entertainment and social media. However, existing models struggle to handle the complexity of Chinese long texts, such as rhetoric, structure, and culture, which restricts their broader application. To bridge this gap, we propose a Chinese Article-style Transfer (CAT-LLM) framework, which addresses the challenges of style transfer in complex Chinese long texts. At its core, CAT-LLM features a bespoke pluggable Text Style Definition (TSD) module that integrates machine learning algorithms to analyze and model article styles at both word and sentence levels. This module acts as a bridge, enabling LLMs to better understand and adapt to the complexities of Chinese article styles. Furthermore, it supports the dynamic expansion of internal style trees, enabling the framework to seamlessly incorporate new and diverse style definitions, enhancing adaptability and scalability for future research and applications. Additionally, to facilitate robust evaluation, we created ten parallel datasets using a combination of ChatGPT and various Chinese texts, each corresponding to distinct writing styles, significantly improving the accuracy of the model evaluation and establishing a novel paradigm for text style transfer research. Extensive experimental results demonstrate that CAT-LLM, combined with GPT-3.5-Turbo, achieves state-of-the-art performance, with a transfer accuracy F1 score of 79.36% and a content preservation F1 score of 96.47% on the "Fortress Besieged" dataset. These results highlight CAT-LLM's innovative contributions to style transfer research, including its ability to preserve content integrity while achieving precise and flexible style transfer across diverse Chinese text domains. Building on these contributions, CAT-LLM presents significant potential for advancing Chinese digital media and facilitating automated content creation.

F. Qi, L. Ni, C. Xu

We introduce a film score generation framework to harmonize visual pixels and music melodies utilizing a latent diffusion model. Our framework processes film clips as input and generates music that aligns with a general theme while offering the capability to tailor outputs to a specific composition style. Our model directly produces music from video, utilizing a streamlined and efficient tuning mechanism on ControlNet. It also integrates a film encoder adept at understanding the film's semantic depth, emotional impact, and aesthetic appeal. Additionally, we introduce a novel, effective yet straightforward evaluation metric to evaluate the originality and recognizability of music within film scores. To fill this gap for film scores, we curate a comprehensive dataset of film videos and legendary original scores, injecting domain-specific knowledge into our data-driven generation model. Our model outperforms existing methodologies in creating film scores, capable of generating music that reflects the guidance of a maestro's style, thereby redefining the benchmark for automated film scores and laying a robust groundwork for future research in this domain. The code and generated samples are available at https://anonymous.4open.science/r/HPM.

T. Y. S. S. Santosh, Apolline Isaia, Shiyu Hong et al.

Rhetorical Role Labeling (RRL) of legal documents is pivotal for various downstream tasks such as summarization, semantic case search and argument mining. Existing approaches often overlook the varying difficulty levels inherent in legal document discourse styles and rhetorical roles. In this work, we propose HiCuLR, a hierarchical curriculum learning framework for RRL. It nests two curricula: Rhetorical Role-level Curriculum (RC) on the outer layer and Document-level Curriculum (DC) on the inner layer. DC categorizes documents based on their difficulty, utilizing metrics like deviation from a standard discourse structure and exposes the model to them in an easy-to-difficult fashion. RC progressively strengthens the model to discern coarse-to-fine-grained distinctions between rhetorical roles. Our experiments on four RRL datasets demonstrate the efficacy of HiCuLR, highlighting the complementary nature of DC and RC.

P. Mack

Abstract:The renaissance witnessed both a large expansion of teaching and composition of rhetoric manuals and a flowering of literature in the sixteenth century. This essay asks what rhetorical theory contributed to renaissance literature. Where some earlier accounts, for example by Cave, Eden and Vickers, focus on the impact of one or two rhetorical doctrines, this essay argues that renaissance writers drew on, adapted and combined a wide range of rhetorical doctrines in thinking about how to persuade and move their audiences. In order to make this argument it sets out sixteen skills taught by renaissance rhetoric which writers could use: thinking about the audience; self-presentation; reusing reading in writing; style and amplification; emotion; pleasing; narrative; character; argument; examples; comparison; contraries; proverbs and axioms; disposition; beginning; and ending. It analyses texts by Erasmus, Tasso, Sidney, Montaigne and Shakespeare to show how the greatest renaissance writers adapted and combined ideas from rhetoric.

Luis Fernando Balceda Requena

La intención de este artículo es mostrar cuáles son los modelos de mundo planteados en dos poemas de Poesía concreta (1952) de Alejandro Romualdo. Para ello, desarrollaremos un análisis retórico-argumentativo a partir de los planteamientos de Stefano Arduini en torno a los campos figurativos, además de otras nociones como los interlocutores, el ethos y las técnicas argumentativas desarrolladas por Perelman. Con este análisis se pretende evidenciar el poder persuasivo de la obra poética de Romualdo, en tanto intenta convencer a los lectores, pero también el afán performativo/modelizador de aquella en el sentido que señala de Manuel Asensi a propósito de los modelos de mundo; esto en la medida que el poeta peruano pretende intervenir abiertamente en la sociedad que representa en sus poemas.

Yiwei Lyu, Paul Pu Liang, Hai Pham et al.

Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence. In this paper, we introduce a large-scale benchmark, StylePTB, with (1) paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as (2) compositions of multiple transfers which allow modeling of fine-grained stylistic changes as building blocks for more complex, high-level transfers. By benchmarking existing methods on StylePTB, we find that they struggle to model fine-grained changes and have an even more difficult time composing multiple styles. As a result, StylePTB brings novel challenges that we hope will encourage future research in controllable text style transfer, compositional models, and learning disentangled representations. Solving these challenges would present important steps towards controllable text generation.

Isao Ishikawa

In this paper, we investigate the boundedness of composition operators defined on a quasi-Banach space continuously included in the space of smooth functions on a manifold. We prove that the boundedness of a composition operator strongly restricts the behavior of the original map, and it provides an effective method to investigate the properties of composition operators using the theory of dynamical system. Consequently, we prove that only affine maps can induce bounded composition operators on any quasi-Banach space continuously included in the space of entire functions of one variable if the function space contains a nonconstant function. We also prove that any polynomial automorphisms except affine transforms cannot induce bounded composition operators on a quasi-Banach space composed of entire functions in the two-dimensional complex affine space under several mild conditions.

Da Ma, Vincent Chow, Karteek Popuri et al.

The latest advances in computer-assisted precision medicine are making it feasible to move from population-wide models that are useful to discover aggregate patterns that hold for group-based analysis to patient-specific models that can drive patient-specific decisions with regard to treatment choices, and predictions of outcomes of treatment. Body Composition is recognized as an important driver and risk factor for a wide variety of diseases, as well as a predictor of individual patient-specific clinical outcomes to treatment choices or surgical interventions. 3D CT images are routinely acquired in the oncological worklows and deliver accurate rendering of internal anatomy and therefore can be used opportunistically to assess the amount of skeletal muscle and adipose tissue compartments. Powerful tools of artificial intelligence such as deep learning are making it feasible now to segment the entire 3D image and generate accurate measurements of all internal anatomy. These will enable the overcoming of the severe bottleneck that existed previously, namely, the need for manual segmentation, which was prohibitive to scale to the hundreds of 2D axial slices that made up a 3D volumetric image. Automated tools such as presented here will now enable harvesting whole-body measurements from 3D CT or MRI images, leading to a new era of discovery of the drivers of various diseases based on individual tissue, organ volume, shape, and functional status. These measurements were hitherto unavailable thereby limiting the field to a very small and limited subset. These discoveries and the potential to perform individual image segmentation with high speed and accuracy are likely to lead to the incorporation of these 3D measures into individual specific treatment planning models related to nutrition, aging, chemotoxicity, surgery and survival after the onset of a major disease such as cancer.

Juan Carlos Iglesias-Zoido

Noticia y comentario de libros recibidos

Victoria Pineda

This review examines the edition of Edmund Bolton’s commentary on the first six books of Tacitus’s Annals by Patricia Osmond and Robert Ulery. The editors’ reading and interpretation of Bolton’s treatise within the historiographic, political and social context of Jacobean England are also described

Xiao-Chang Liu, Xuan-Yi Li, Ming-Ming Cheng et al.

Neural style transfer (NST), where an input image is rendered in the style of another image, has been a topic of considerable progress in recent years. Research over that time has been dominated by transferring aspects of color and texture, yet these factors are only one component of style. Other factors of style include composition, the projection system used, and the way in which artists warp and bend objects. Our contribution is to introduce a neural architecture that supports transfer of geometric style. Unlike recent work in this area, we are unique in being general in that we are not restricted by semantic content. This new architecture runs prior to a network that transfers texture style, enabling us to transfer texture to a warped image. This form of network supports a second novelty: we extend the NST input paradigm. Users can input content/style pair as is common, or they can chose to input a content/texture-style/geometry-style triple. This three image input paradigm divides style into two parts and so provides significantly greater versatility to the output we can produce. We provide user studies that show the quality of our output, and quantify the importance of geometric style transfer to style recognition by humans.

Aviv Gabbay, Yedid Hoshen

Unsupervised image-to-image translation methods have achieved tremendous success in recent years. However, it can be easily observed that their models contain significant entanglement which often hurts the translation performance. In this work, we propose a principled approach for improving style-content disentanglement in image-to-image translation. By considering the information flow into each of the representations, we introduce an additional loss term which serves as a content-bottleneck. We show that the results of our method are significantly more disentangled than those produced by current methods, while further improving the visual quality and translation diversity.

Maria Brilliant

Eithan Orkibi

This article explores the rhetoric of auto-victimization in political discourse. While “victimization” is generally defined as the process by which individuals or group as culturally constructed or socially acknowledged as victim, “auto-victimization” is the discursive practice by which a speaker constructs his or her own image or identity as victim. Drawing on the theoretical framework of the interactionalist approach to victimology in social sciences, the study examines three constitutive dimensions of a political leader, i.e, Benjamins Netanyahu’s rhetoric of auto-victimization in response to corruption charges: emotional mobilization, cultural resonance, and appeal to collective identity. The analysis shows that Netanyahu projects an image of a victim of persecution, while assimilating his personal story to the constitutive narrative of the Israeli right, self-perceived as a historically marginalized and oppressed by the Israeli left. Netanyahu’s auto-victimization thus transforms into a symbolic figure of the Israeli right’s tradition of victimization, and constitutes an emergency call to defend Netanyahu in order to protect the entire political camp. The results of the analysis correspond to the discursive practices observed by cultural victimology, but they also echo some of the rhetorical strategies associated with contemporary populist political discourse.