OpenRaiser/Intern-Atlas huggingface dataset:
High-Context Empathy in Conversations for Large Language Models
Authors: Yuyan Chen, Lei Xia, Jinghan Cao, Zhendong Hou, Weinan Dai, Zhixu Li
Venue: CIKM
Year: 2025

Large Language Models (LLMs) exhibit remarkable capabilities across various downstream tasks, including empathetic dialogues. However, a non-trivial question arises: Do they possess high-context empathy and can they generate emotional interactions with humans? High-context empathy, which tends to be more indirect and concise like Chinese-style empathy, differs from the current empathy capabilities of LLMs. These capabilities are predominantly low-context empathy, which is often direct and lengthy, resembling English-style empathy. In this paper, We first construct a comprehensive Chinese High-context Empathy Dialogue dataset (HED), which consists of emotional, role-based emotional, personality-based emotional, and role-personality-based emotional dialogues. Next, we explore whether LLMs have high-context empathy in conversations. After that, we propose an innovative High-context Empathy Network (HEN) to improve LLMs' capabilities in generating high-context empathetic responses. Our empirical study demonstrates that there is much room for LLMs in generating high-context empathetic responses, and the proposed HEN can not only significantly improve LLMs' capabilities in generating high-context empathetic responses, but also has positive effects for LLMs in solving similar sentiment-related tasks.
ConsensNet: A Unified Consensus-Centric Framework for Incomplete Multi-View Clustering
Authors: Yifei Chen, Xiaolin Xiao, Yue-Jiao Gong
Venue: CIKM
Year: 2025

Incomplete Multi-View Clustering (IMVC) addresses the challenge of missing data by leveraging available information and effectively mining cross-view relationships. While contrastive learning has recently been introduced into IMVC for discriminative representation learning, existing methods typically adopt pairwise contrastive strategies with view-specific reconstruction and heuristic fusion schemes. These approaches are generally suboptimal when facing high missing-view ratios and struggle to capture latent cross-view dependencies. To overcome these limitations, we propose ConsensNet, a unified consensus-centric framework for IMVC. This is achieved through a unified architecture that integrates contrastive cross-view alignment, consensus prediction, and attention-aware fusion. By aligning all available views into a shared semantic space, ConsensNet effectively captures latent cross-view dependencies without requiring high-quality view completion. Moreover, the attention-aware fusion mechanism dynamically assigns weights to each view based on its relevance to the consensus, thereby reducing the impact of noisy or weakly correlated views. Extensive experiments on multiple datasets demonstrate that ConsensNet consistently outperforms state-of-the-art IMVC methods, particularly under high missing-view scenarios, highlighting its robustness and practical significance.
G2IFS: Global-to-Instance Feature Selection in Deep Recommender System
Authors: Lijin Chen, Linjing You, Jiabao Lu, Xiayuan Huang, Xiangli Nie
Venue: CIKM
Year: 2025

Feature selection plays a vital role in recommender systems by identifying informative features for accurate prediction. While adaptive methods like AdaFS select instance-wise features based on sample variability, they often overlook globally important features and suffer from limited transferability. To address these limitations, we propose G2IFS (Global-to-Instance Feature Selection), a novel framework that integrates global distributional patterns with instance-level adaptation for more robust and generalizable feature selection. G2IFS consists of an online statistics module, a main scoring network, and a non-parametric Gaussian mixture module. The online statistics module maintains global estimates of class-wise statistics to compute Fisher scores, which guide the scoring network in learning instance-specific feature importance. The Gaussian module further mitigates co-adaptation and improves transferability. Extensive experiments across diverse recommendation models and real-world datasets show that G2IFS consistently outperforms state-of-the-art baselines in terms of accuracy, efficiency, and transferability. In-depth analysis further reveals that various global importance signals-when integrated into traditional methods like AdaFS-consistently lead to significant performance improvements, underscoring the general effectiveness of combining global and instance-level signals in recommender system feature selection. The code is available at https://github.com/youlj109/G2IFS.
Toward Secure Federated Partial Label Learning Against Poisoning Attacks
Authors: Xubin Chen, Zhengjie Yang, Xinyi Sheng, Sen Fu, Wei Bao
Venue: CIKM
Year: 2025

How to defend against attacks in Federated Partial Label Learning (FedPLL) is a brand new and challenging question in machine learning security due to stealthy and efficient attack behaviors of adversaries. In this paper, we systematically study this problem by developing an Adaptive Partial Label Attack (APLA) which subtly manipulates the candidate label set of the data sample. To defend against APLA, we develop the RobustFedPLL framework incorporating three modules: (1) in preliminary clustering, we implement a Gaussian Mixture Model (GMM) and a moving average mechanism to identify clients' confidence; (2) in representation contrasting, we develop a contrast-based algorithm to obtain clients' model feature representations; (3) in final clustering, we utilize mainstream clustering algorithms to finally distinguish adversaries. Experiments of RobustFedPLL and SOTA defense algorithms based on two datasets are conducted, demonstrating the superiority of RobustFedPLL under various experimental settings.
Efficient Mask Learning for Language Model Fine-Tuning
Authors: Minping Chen, Ruijia Yang, Zeyi Wen
Venue: CIKM
Year: 2025

Parameter-efficient fine-tuning (PEFT) of pre-trained language models (PLMs) has shown promising results by updating significantly fewer parameters than full fine-tuning. Masking-based fine-tuning is one type of PEFT methods, by freezing the majority of the model parameters during fine-tuning. Existing masking-based fine-tuning methods either need to manually select the trainable parameters (heuristic-based), or perform mask learning to adaptively select the trainable parameters with high memory and computation cost. To address these problems, this paper proposes Low-Rank based Efficient Mask Learning (LoReML). LoReML performs mask learning based on low-rank decomposition and matrix reconstruction with a small ratio of new parameters. After mask learning, LoReML uses the scaled intermediate results in mask learning as warm start initialization to boost the model quality, then freezes the masked parameters accordingly, and fine-tunes the PLM. Moreover, LoReML exploits sparse training techniques to enhance the memory efficiency in masking-based fine-tuning. Experimental results across various tasks and pre-trained backbones demonstrate that LoReML can notably outperform existing heuristic-based methods. Moreover, LoReML achieves competitive or better performance compared with the adaptive mask learning methods, while improving memory and computation efficiency by over 50% in mask learning.
M-LLM3REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs
Authors: Lining Chen, Qingwen Zeng, Huaming Chen
Venue: CIKM
Year: 2025

Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods, i.e., content-based filtering, collaborative filtering, and deep learning, have achieved impressive results in recommendation systems. However, the cold-start and sparse-data scenarios are still challenging to deal with. Existing solutions either generate pseudo-interaction sequence, which often introduces redundant or noisy signals, or rely heavily on semantic similarity, overlooking dynamic shifts in user motivation. To address these limitations, this paper proposes a novel recommendation framework, termed M-LLM3REC, which leverages large language models for deep motivational signal extraction from limited user interactions. M-LLM3REC comprises three integrated modules: the Motivation-Oriented Profile Extractor (MOPE), Motivation-Oriented Trait Encoder (MOTE), and Motivational Alignment Recommender (MAR). By emphasizing motivation-driven semantic modeling, M-LLM3REC demonstrates robust, personalized, and generalizable recommendations, particularly boosting performance in cold-start situations in comparison with the state-of-the-art frameworks.
T-Stars-Poster: A Framework for Product-Centric Advertising Image Design
Authors: Hongyu Chen, Min Zhou, Jing Jiang, Jiale Chen, Yang Lu, Zihang Lin, Bo Xiao, Tiezheng Ge, Bo Zheng
Venue: CIKM
Year: 2025

Creating advertising images is often a labor-intensive and time-consuming process. Can we automatically generate such images using basic product information like a product foreground image, taglines, and a target size? Existing methods mainly focus on parts of the problem and lack a comprehensive solution. To bridge this gap, we propose a novel product-centric framework for advertising image design called T-Stars-Poster. It consists of four sequential stages to highlight product foregrounds and taglines while achieving overall image aesthetics: prompt generation, layout generation, background image generation, and graphics rendering. Different expert models are designed and trained for the first three stages: First, a visual language model (VLM) generates background prompts that match the products. Next, a VLM-based layout generation model arranges the placement of product foregrounds, graphic elements (taglines and decorative underlays), and various nongraphic elements (objects from the background prompt). Following this, an SDXL-based model can simultaneously accept prompts, layouts, and foreground controls to generate images. To support T-Stars-Poster, we create two corresponding datasets with over 50,000 labeled images. Extensive experiments and online A/B tests demonstrate that T-Stars-Poster can produce more visually appealing advertising images.
More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
Authors: Evan Chen, Run-Jun Zhan, Yan-Bai Lin, Hung-Hsuan Chen
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have revolutionized natural language processing, yet concerns persist regarding their tendency to reflect or amplify social biases. This study introduces a novel evaluation framework to uncover gender biases in LLMs: using free-form storytelling to surface biases embedded within the models. A systematic analysis of ten prominent LLMs shows a consistent pattern of overrepresenting female characters across occupations, likely due to supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Paradoxically, despite this overrepresentation, the occupational gender distributions produced by these LLMs align more closely with human stereotypes than with real-world labor data. This highlights the challenge and importance of implementing balanced mitigation measures to promote fairness and prevent the establishment of potentially new biases. We release the prompts and LLM-generated stories at GitHub.
Neighbor-enhanced Graph Pre-training and Prompt Learning Framework for Fraud Detection
Authors: Ziyang Cheng, Jie Yang, Yixin Song, Dawei Cheng, Guang Yang, Bo Wang
Venue: CIKM
Year: 2025

Nowadays, as more users turn to WeChat Pay and other e-commerce platforms for transactions, an increasing number of fraudsters are being attracted to these platforms to conduct fraudulent activities, thereby stealing money. To address this issue, Graph Neural Networks (GNNs) have been widely adopted and have shown great success. However, with the rise of various transaction methods, users are increasingly engaging in multiple transaction networks, which creates a new scenario that requires models to detect fraud across these diverse networks. Unfortunately, current GNN-based fraud detection strategies often exhibit suboptimal performance and high time complexity in this evolving scenario, as they typically can handle only one transaction network at a time. Recently, advancements in graph prompt learning have demonstrated great success in managing various types of graph data and improving the generalization capabilities of the model, showing great promise for addressing this new fraud detection scenario. Nevertheless, the practical application of graph prompt learning in real-world fraud detection is still constrained, as they may exhibit bias when dealing with multiplex transaction networks and may fail to model the intrinsic relationships between nodes and their neighbors, which is crucial for effective fraud detection. To address these two challenges, we propose GPCF, an efficient graph pre-training and prompt learning framework. GPCF first incorporates a meta-learning-based strategy within neighbor-enhanced contrastive learning to pre-train the GNN model across diverse transaction networks. Then it aligns fraud detection tasks with the well-pre-trained model by simply fine-tuning the prompts. Extensive experiments demonstrate that GPCF achieves state-of-the-art results on open-access fraud and transaction datasets, as well as on real-world fraud datasets from WeChat Pay, one of the largest e-commerce platforms globally, showing the effectiveness of GPCF in practical applications.
Evolving Graph-Based Context Modeling for Multi-Turn Conversational Retrieval-Augmented Generation
Authors: Yiruo Cheng, Hongjin Qian, Fengran Mo, Yongkang Wu, Zhonghua Li, Qi Ye, Ji-Rong Wen, Zhicheng Dou
Venue: CIKM
Year: 2025

Conversational Retrieval-Augmented Generation (RAG) systems enhance user interactions by integrating large language models (LLMs) with external knowledge retrieval. However, multi-turn conversations present significant challenges, including implicit user intent and noisy context, which hinder accurate retrieval and response generation. Existing approaches often struggle with the unstructured conversational context and fail to model explicit relations among conversational turns. Moreover, they do not leverage historically relevant passages effectively. To overcome these limitations, we propose EvoRAG, a novel framework that maintains an evolving knowledge graph aligned with the unstructured conversational context. This graph explicitly captures relations among user queries, system responses, and relevant passages across conversational turns, serving as a structured representation of the context. EvoRAG includes three key components: (1) a dual-path retrieval module for context denoising, (2) a unified knowledge integration module for query rewriting and summarization, and (3) a graph-enhanced RAG module for accurate retrieval and response generation. Experiments on four public conversational RAG datasets show that EvoRAG significantly outperforms strong baselines, particularly in handling topic shifts and long dialogue contexts.
VideoAVE: A Multi-Attribute Video-to-Text Attribute Value Extraction Dataset and Benchmark Models
Authors: Ming Cheng, Tong Wu, Jiazhen Hu, Jiaying Gong, Hoda Eldardiry
Venue: CIKM
Year: 2025

Attribute Value Extraction (AVE) is important for structuring product information in e-commerce. However, existing AVE datasets are primarily limited to text-to-text or image-to-text settings, lacking support for product videos, diverse attribute coverage, and public availability. To address these gaps, we introduce VideoAVE, the first publicly available video-to-text e-commerce AVE dataset across 14 different domains and covering 172 unique attributes. To ensure data quality, we propose a post-hoc CLIP-based Mixture of Experts filtering system (CLIP-MoE) to remove the mismatched video-product pairs, resulting in a refined dataset of 224k training data and 25k evaluation data. In order to evaluate the usability of the dataset, we further establish a comprehensive benchmark by evaluating several state-of-the-art video vision language models (VLMs) under both attribute-conditioned value prediction and open attribute-value pair extraction tasks. Our results analysis reveals that video-to-text AVE remains a challenging problem, particularly in open settings, and there is still room for developing more advanced VLMs capable of leveraging effective temporal information. The dataset and benchmark code for VideoAVE are available at: https://github.com/gjiaying/VideoAVE.
PP-STAT: An Efficient Privacy-Preserving Statistical Analysis Framework using Homomorphic Encryption
Authors: Hyunmin Choi
Venue: CIKM
Year: 2025

With the widespread adoption of cloud computing, the need for outsourcing statistical analysis to third-party platforms is growing rapidly. However, handling sensitive data such as medical records and financial information in cloud environments raises serious privacy concerns. In this paper, we present PP-STAT, a novel and efficient Homomorphic Encryption (HE)-based framework for privacy-preserving statistical analysis. HE enables computations to be performed directly on encrypted data without revealing the underlying plaintext. PP-STAT supports advanced statistical measures, including Z-score normalization, skewness, kurtosis, coefficient of variation, and Pearson correlation coefficient, all computed securely over encrypted data. To improve efficiency, PP-STAT introduces two key optimizations: (1) a Chebyshev-based approximation strategy for initializing inverse square root operations, and (2) a pre-normalization scaling technique that reduces multiplicative depth by folding constant scaling factors into mean and variance computations. These techniques significantly lower computational overhead and minimize the number of expensive bootstrapping procedures. Our evaluation on real-world datasets demonstrates that PP-STAT achieves high numerical accuracy, with mean relative error (MRE) below 2.4×10-4. Notably, the encrypted Pearson correlation coefficient between the smoker attribute and charges reaches 0.7873, with an MRE of 2.86×10-4. These results confirm the practical utility of PP-STAT for secure and precise statistical analysis in privacy-sensitive domains.
Reference-Aligned Retrieval-Augmented Question Answering over Heterogeneous Proprietary Documents
Authors: Nayoung Choi, Grace Byun, Andrew Chung, Ellie S. Paek, Shinsun Lee, Jinho D. Choi
Venue: CIKM
Year: 2025

Proprietary corporate documents contain rich domain-specific knowledge, but their overwhelming volume and disorganized structure make it difficult even for employees to access the right information when needed. For example, in the automotive industry, vehicle crash-collision tests-each costing hundreds of thousands of dollars-produce highly detailed documentation. However, retrieving relevant content during decision-making remains time-consuming due to the scale and complexity of the material. While Retrieval-Augmented Generation (RAG)-based Question Answering (QA) systems offer a promising solution, building an internal RAG-QA system poses several challenges: (1) handling heterogeneous multi-modal data sources, (2) preserving data confidentiality, and (3) enabling traceability between each piece of information in the generated answer and its original source document. To address these, we propose a RAG-QA framework for internal enterprise use, consisting of: (1) a data pipeline that converts raw multi-modal documents into a structured corpus and QA pairs, (2) a fully on-premise, privacy-preserving architecture, and (3) a lightweight reference matcher that links answer segments to supporting content. Applied to the automotive domain, our system improves factual correctness (+1.79, +1.94), informativeness (+1.33, +1.16), and helpfulness (+1.08, +1.67) over a non-RAG baseline, based on 1-5 scale ratings from both human and LLM judge. The system was deployed internally for pilot testing and received positive feedback from employees.
Learnable Orthogonal Decomposition for Non-Regressive Prediction for PDE
Authors: Yun Young Choi, Kyujin Han, Joohwan Ko, Sangwook Baek, Seunghwan Lee
Venue: CIKM
Year: 2025

Modeling the spatio-temporal evolution of complex physical sys- tems remains a fundamental challenge in both deep learning and sci- entific computing. While recent methods such as Transformers and Neural Operators have shown promise in learning PDE solutions, their reliance on auto-regressive forecasting often increases compu- tational overhead and accumulates prediction errors over time. In this paper, we propose Learnable Orthogonal Decomposition (LOD), a non-regressive framework that integrates ideas from classical Proper Orthogonal Decomposition (POD) with modern deep learn- ing. LOD first performs parameter-wise POD: at each time step, we apply POD to an ensemble of PDE solutions generated under differ- ent physical parameters, yielding a time-indexed set of orthonormal spatial bases. These bases initialize a learnable dictionary and are refined during end-to-end training. Given only a short prefix of ini- tial conditions for a target parameter setting, a neural encoder pre- dicts-in a single shot-the entire trajectory of parameter-wise POD coefficients. The final solution field is reconstructed by combining the predicted coefficients with the learned bases, avoiding error ac- cumulation inherent to auto-regressive strategies. Comprehensive experiments on various PDE benchmark datasets demonstrate that LOD achieves state-of-the-art accuracy while significantly reducing computational costs.
Human-Centric AI: From Explainability and Trustworthiness to Actionable Ethics
Authors: Jaesik Choi, Bohyung Han, Myoung-Wan Koo, Kyungman Bae, Chang D. Yoo, Simon S. Woo, Wojciech Samek
Venue: CIKM
Year: 2025

To address the potential risks of AI while supporting innovation and ensuring responsible adoption, there is an urgent need for clear governance frameworks grounded in human-centric values. It is imperative that AI systems operate in ways that are transparent, trustworthy, and ethically sound. Developing truly human-centric AI goes beyond technical innovation. It requires interdisciplinary collaboration and diverse perspectives. This workshop will explore key challenges and emerging solutions in the development of human-centric AI, with a focus on explainability, trustworthiness, fairness, and privacy. We welcome both theoretical contributions and practical case studies that demonstrate how human-centered principles are realized in real-world AI systems. The official workshop webpage is available at https://xai.kaist.ac.kr/Workshop/hcai2025/, which provides comprehensive information about the program.