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Despite strong performance of Multimodal Large Language Models (MLLMs) on multimodal tasks, predicting whether and why an image is persuasive remains challenging. We first show that prompting MLLMs to reason before prediction does not…

计算机视觉与模式识别 · 计算机科学 2026-05-12 Naeun Lee , Hyunjong Kim , Sunghwan Choi , Injin Kong , Yohan Jo

Warning: This research studies AI persuasion and bias amplification that could be misused; all experiments are for safety evaluation. Large Language Models (LLMs) now generate convincing, human-like text and are widely used in content…

计算与语言 · 计算机科学 2025-08-25 Saumya Roy

Recent advances in vision-language models (VLMs) have enabled impressive multi-modal reasoning and understanding. Yet, whether these models truly grasp visual persuasion-how visual cues shape human attitudes and decisions-remains unclear.…

计算与语言 · 计算机科学 2025-11-24 Gyuwon Park

Previous work has showcased the intriguing capabilities of Large Language Models (LLMs) in instruction-following and rhetorical fluency. However, systematic exploration of their dual capabilities to autonomously persuade and resist…

计算与语言 · 计算机科学 2025-06-10 Tianjie Ju , Yujia Chen , Hao Fei , Mong-Li Lee , Wynne Hsu , Pengzhou Cheng , Zongru Wu , Zhuosheng Zhang , Gongshen Liu

Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user perspectives and subsequently influence their decisions on…

人工智能 · 计算机科学 2024-09-04 Ganesh Prasath Ramani , Shirish Karande , Santhosh V , Yash Bhatia

Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. Recent work has used linear probes, lightweight tools for analyzing model…

计算与语言 · 计算机科学 2025-08-08 Brandon Jaipersaud , David Krueger , Ekdeep Singh Lubana

The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, enabling fully-automated, personalized, and interactive content generation at an unprecedented scale. In this paper, we…

计算与语言 · 计算机科学 2026-04-22 Sander Noels , Alexander Rogiers , Maarten Buyl , Tijl De Bie

We are exposed to much information trying to influence us, such as teaser messages, debates, politically framed news, and propaganda - all of which use persuasive language. With the recent interest in Large Language Models (LLMs), we study…

计算与语言 · 计算机科学 2025-02-24 Amalie Brogaard Pauli , Isabelle Augenstein , Ira Assent

Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content, such as images and textual metadata. While effective, it remains unclear whether their gains stem from true multimodal understanding…

信息检索 · 计算机科学 2025-08-07 Claudio Pomo , Matteo Attimonelli , Danilo Danese , Fedelucio Narducci , Tommaso Di Noia

Connecting text and visual modalities plays an essential role in generative intelligence. For this reason, inspired by the success of large language models, significant research efforts are being devoted to the development of Multimodal…

Usability evaluation is an essential method to support the design of effective and intuitive user interfaces (UIs). However, it commonly relies on resource-intensive, expert-driven methods, which limit its accessibility, especially for…

The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in natural language understanding and visual processing,…

Achieving deep alignment between vision and language remains a central challenge for Multimodal Large Language Models (MLLMs). These models often fail to fully leverage visual input, defaulting to strong language priors. Our approach first…

计算机视觉与模式识别 · 计算机科学 2025-07-03 Aarti Ghatkesar , Ganesh Venkatesh

Multimodal large language models (MLLMs) often struggle to ground reasoning in perceptual evidence. We present a systematic study of perception strategies-explicit, implicit, visual, and textual-across four multimodal benchmarks and two…

计算机视觉与模式识别 · 计算机科学 2025-09-30 Yizhuo Ding , Mingkang Chen , Zhibang Feng , Tong Xiao , Wanying Qu , Wenqi Shao , Yanwei Fu

In recent years, multimodal large language models (MLLMs) have shown remarkable capabilities in tasks like visual question answering and common sense reasoning, while visual perception models have made significant strides in perception…

计算机视觉与模式识别 · 计算机科学 2024-06-25 Guanqun Wang , Xinyu Wei , Jiaming Liu , Ray Zhang , Yichi Zhang , Kevin Zhang , Maurice Chong , Shanghang Zhang

Detecting persuasion in argumentative text is a challenging task with important implications for understanding human communication. This work investigates the role of persuasion strategies - such as Attack on reputation, Distraction, and…

计算与语言 · 计算机科学 2026-01-16 Tiziano Labruna , Arkadiusz Modzelewski , Giorgio Satta , Giovanni Da San Martino

Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. This paper presents a multi-LLM communication framework designed to enhance…

With the advent of Large Language Models (LLMs) possessing increasingly impressive capabilities, a number of Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. Such models condition generated text on…

计算机视觉与模式识别 · 计算机科学 2024-06-11 Phillip Howard , Anahita Bhiwandiwalla , Kathleen C. Fraser , Svetlana Kiritchenko

In today's world, emotional support is increasingly essential, yet it remains challenging for both those seeking help and those offering it. Multimodal approaches to emotional support show great promise by integrating diverse data sources…

Despite the rapid progress of multimodal large language models (MLLMs), they have largely overlooked the importance of visual processing. In a simple yet revealing experiment, we interestingly find that language-only models, when provided…

计算机视觉与模式识别 · 计算机科学 2025-09-30 Yuting Li , Lai Wei , Kaipeng Zheng , Jingyuan Huang , Guilin Li , Bo Wang , Linghe Kong , Lichao Sun , Weiran Huang
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