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20242026
most citedWhen Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity

1 citations · 1 across the 11 of their papers we have counts for

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cs.CL2026

New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs

Shiyao Cui, QingLin Zhang, Di Wang +7

Neologisms, emerging terms in meaning or form, can serve as new vehicles for toxic expression, like "country girl" as a stigmatizing label targeting feminism. Such toxic neologisms…

cs.CL2026

Safe Alone, Unsafe Together: Safeguarding Against Implicit Toxicity When Benign Images Combine

Jiaxian Lv, Shiyao Cui, Yingkang Wang +3

Multi-image content has become an increasingly prevalent form of visual communication in social media, giving rise to a new safety issue, multi-image implicit toxicity (MIIT), wher…

cs.CL2025

Speculating LLMs' Chinese Training Data Pollution from Their Tokens

Qingjie Zhang, Di Wang, Haoting Qian +7

Tokens are basic elements in the datasets for LLM training. It is well-known that many tokens representing Chinese phrases in the vocabulary of GPT (4o/4o-mini/o1/o3/4.5/4.1/o4-min…

cs.CL2025

Understanding the Dilemma of Unlearning for Large Language Models

Qingjie Zhang, Haoting Qian, Zhicong Huang +5

Unlearning seeks to remove specific knowledge from large language models (LLMs), but its effectiveness remains contested. On one side, "forgotten" knowledge can often be recovered…

cs.CL2025★ 1 cited

When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity

Shiyao Cui, Xijia Feng, Yingkang Wang +6

Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across context…

cs.CL2025

Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings

Shujian Yang, Shiyao Cui, Chuanrui Hu +5

Detecting toxic content using language models is important but challenging. While large language models (LLMs) have demonstrated strong performance in understanding Chinese, recent…

cs.CL2024

Understanding the Dark Side of LLMs' Intrinsic Self-Correction

Qingjie Zhang, Di Wang, Haoting Qian +7

Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability. However, recent works show that LLMs' intrinsic se…