31 citations · 63 across the 16 of their papers we have counts for
16 papers
Whitewashing Hate, Smearing Harmless Content: Annotator-Style Rebuttal Attacks on LLM-Based Moderation
Junyu Lu, Kaiyuan Liu, Kaichun Wang +8
Large language models (LLMs) are increasingly used for hate speech moderation, often within human--AI workflows in which reviewers provide feedback before a final decision. Such fe…
Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos
Junyu Lu, Deyi Ji, Liqun Liu +9
Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus on binary classification an…
Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes
Weiming Wang, Junyu Lu, Han Wang +5
Research on harmful meme detection has garnered significant attention, resulting in the development of numerous datasets and methods. However, progress in detecting Chinese harmful…
Harder to Defend: Towards Chinese Toxicity Attacks via Implicit Enhancement and Obfuscation Rewriting
Jingyi Kang, Junyu Lu, Bo Xu +4
Large language models (LLMs) require robust toxicity evaluation beyond explicit wording. This setting remains underexplored in Chinese, where toxicity may combine semantic indirect…
IP2: Entity-Guided Interest Probing for Personalized News Recommendation
Youlin Wu, Yuanyuan Sun, Xiaokun Zhang +4
News recommender systems aim to provide personalized news reading experiences for users based on their reading history. Behavioral science studies suggest that screen-based news re…
Rethinking Contrastive Learning in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma +3
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailin…
A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective
Xiaokun Zhang, Bo Xu, Chenliang Li +4
Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts in…
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement
Junyu Lu, Kai Ma, Kaichun Wang +5
Large Language Models (LLMs) have become essential for offensive language detection, yet their ability to handle annotation disagreement remains underexplored. Disagreement samples…
Towards Comprehensive Detection of Chinese Harmful Memes
Junyu Lu, Bo Xu, Xiaokun Zhang +5
This paper has been accepted in the NeurIPS 2024 D & B Track. Harmful memes have proliferated on the Chinese Internet, while research on detecting Chinese harmful memes significant…
PclGPT: A Large Language Model for Patronizing and Condescending Language Detection
Hongbo Wang, Mingda Li, Junyu Lu +5
Disclaimer: Samples in this paper may be harmful and cause discomfort! Patronizing and condescending language (PCL) is a form of speech directed at vulnerable groups. As an essenti…
Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning
Yijie Shu, Xiaokun Zhang, Youlin Wu +3
News recommendation emerges as a primary means for users to access content of interest from the vast amount of news. The title clickbait extensively exists in news domain and incre…
Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect
Junyu Lu, Bo Xu, Xiaokun Zhang +4
Current methods of toxic language detection (TLD) typically rely on specific tokens to conduct decisions, which makes them suffer from lexical bias, leading to inferior performance…
FineRec:Exploring Fine-grained Sequential Recommendation
Xiaokun Zhang, Bo Xu, Youlin Wu +3
Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews fo…
Disentangling ID and Modality Effects for Session-based Recommendation
Xiaokun Zhang, Bo Xu, Zhaochun Ren +3
Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence pa…
Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives
Haohao Zhu, Xiaokun Zhang, Junyu Lu +7
Textual personality detection aims to identify personality characteristics by analyzing user-generated content on social media platforms. Extensive psychological literature highlig…
Side Information-Driven Session-based Recommendation: A Survey
Xiaokun Zhang, Bo Xu, Chenliang Li +3
The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate v…