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20222026
most citedYou Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content

8 citations · 25 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis

Bo Shao, Yihan Ma, Yiting Qu +2

Online memes are often carriers of unsafe content such as disinformation and propaganda. Their rapid evolution in response to news events and strong dependence on contextual inform…

cs.CR2026

EchoCoT: Extracting Hidden Chain-of-Thought from Large Reasoning Models

Yiting Qu, Ziqing Yang, Chi Cui +3

Hidden chain-of-thought (CoT) traces, especially those from frontier proprietary large reasoning models (LRMs), are valuable model assets. Yet whether these hidden CoTs can be dire…

cs.LG2026

GEO-Flag: Detecting and Measuring GEO-Optimized Web Content

Junjie Chu, Ye Leng, Mingjie Li +3

Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines. This can give strategically optimized…

cs.CV2026

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation

Ye Leng, Junjie Chu, Yiting Qu +3

Picture books and comics have long been used to disseminate hateful narratives because they are easily understood even by children, as exemplified by the notorious Nazi propaganda…

cs.CR2026

Understanding LLM Behavior When Encountering User-Supplied Harmful Content in Harmless Tasks

Junjie Chu, Yiting Qu, Ye Leng +4

Large Language Models (LLMs) are increasingly trained to align with human values, primarily focusing on task level, i.e., refusing to execute directly harmful tasks. However, a sub…