31 citations · 92 across the 15 of their papers we have counts for
15 papers
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…
Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis
Junyu Lu, Deyi Ji, Xuanyi Liu +5
Large language models for subjectivity analysis are typically trained with aggregated labels, which compress variations in human judgment into a single supervision signal. This par…
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…
Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation
Bo Zhang, Hui Ma, Dailin Li +4
Large language models (LLMs) demonstrate remarkable text comprehension and generation capabilities but often lack the ability to utilize up-to-date or domain-specific knowledge not…
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…
Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation
Hui Ma, Bo Zhang, Bo Xu +3
Empathetic response generation, aiming to understand the user's situation and feelings and respond empathically, is crucial in building human-like dialogue systems. Traditional app…
Distilling Implicit Multimodal Knowledge into Large Language Models for Zero-Resource Dialogue Generation
Bo Zhang, Hui Ma, Jian Ding +3
Integrating multimodal knowledge into large language models (LLMs) represents a significant advancement in dialogue generation capabilities. However, the effective incorporation of…
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…
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma +3
Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor…