OpenRaiser/Intern-Atlas huggingface dataset:
Federated Approximate Query Processing Based on Deep Models
Authors: Yutong Xie, Qingzhi Ma, Lei Zhao, An Liu
Venue: CIKM
Year: 2025

Data isolation poses a significant challenge to efficient big data query processing, as data providers are often reluctant to share their raw data due to security concerns. Current federated query systems address this issue by employing Secure Multi-Party Computation (SMC) and Differential Privacy (DP) to facilitate secure and collaborative computation. However, these privacy-preserving methods rely on cryptographic protocols, which introduce substantial computational overhead, slowing query processing by up to 1,000 times compared to plaintext queries. While sampling methods have been explored to enhance federated query systems, they frequently fail to strike a balance between accuracy and speed. To address the limitations above, we propose a secure federated approximate query system based on a deep classifier (SAQDC). This system utilizes deep learning techniques to accelerate query processing while integrating SMC and Differential Privacy to achieve an optimal balance between privacy and efficiency by allowing each data provider to train classifiers using Multi-Layer Perceptron (MLP) and Deep Set architectures, which predict query relative errors across different modules.Based on the prediction errors generated by the classifier, queries are assigned to the most appropriate approximate query model and the differential privacy parameters are adjusted to enhance query accuracy. This approach enhances query speed, preserves accuracy, and effectively mitigates malicious differential privacy attacks. We demonstrate SAQDC's superior performance through extensive experiments on three large-scale datasets.
A Robust and High-Efficiency Active Clustering Framework with Multi-User Collaboration
Authors: Wen-Bo Xie, Tian Zou, Tao Deng, Xuan-Lin Zhu, Xun Fu, Qiu-Yu Wang, Bin Chen, Xin Wang
Venue: CIKM
Year: 2025

Active constraint-based clustering enhances semi-supervised clustering through a machine-led interaction process. This approach dynamically selects the most informative constraints to query, minimizing the number of human annotations required. Existing methods face three key challenges in real-world applications: scalability, timeliness, and robustness against user annotation errors. In this work, we propose a robust and high-efficiency Active Clustering framework with Multi-user Collaboration (ACMC). ACMC constructs a diffusion tree using the nearest-neighbor technique and employs a multi-user online collaboration framework to iteratively refine clustering results. In each iteration: (a) nodes with high uncertainty and representativeness are selected in batch; (b) well-designed multi-user asynchronous query categorizes selected nodes using neighborhood sets, reducing individual workloads and improving overall timeliness; (c) user-provided constraints and newly discovered categories are synchronized, with user confidences dynamically updated to enhance robustness against erroneous annotations; (d) categorized nodes, stored in neighborhood sets, serve as sources in the diffusion tree to refine the clusters. Experimental results demonstrate that ACMC outperforms baseline methods in terms of clustering quality, scalability, and robustness against user annotation errors.
LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking
Authors: Amy Xin, Yunjia Qi, Zijun Yao, Fangwei Zhu, Kaisheng Zeng, Bin Xu, Lei Hou, Juanzi Li
Venue: CIKM
Year: 2025

Specialized entity linking (EL) models are well-trained at mapping mentions to unique knowledge base (KB) entities according to a given context. However, specialized EL models struggle to disambiguate long-tail entities due to their limited training data. Meanwhile, extensively pre-trained large language models (LLMs) possess broader knowledge of uncommon entities. Yet, with a lack of specialized EL training, LLMs frequently fail to generate accurate KB entity names, limiting their standalone effectiveness in EL. With the observation that LLMs are more adept at context generation instead of EL execution, we introduce LLM-Augmented Entity Linking (LLMAEL), the first framework to enhance specialized EL models with LLM data augmentation. LLMAEL leverages off-the-shelf, tuning-free LLMs as context augmenters, generating entity descriptions to serve as additional input for specialized EL models. Experiments show that LLMAEL sets new state-of-the-art results across 6 widely adopted EL benchmarks: compared to prior methods that integrate tuning-free LLMs into EL, LLMAEL achieves an absolute 8.9% gain in EL accuracy. We release our code and datasets.
Multi-Turn Interactions for Text-to-SQL with Large Language Models
Authors: Guanming Xiong, Junwei Bao, Hongfei Jiang, Yang Song, Wen Zhao
Venue: CIKM
Year: 2025

This study explores text-to-SQL parsing by leveraging the powerful reasoning capabilities of large language models (LLMs). Despite recent advancements, existing LLM-based methods are still inefficient and struggle to handle cases with wide tables effectively. Furthermore, current interaction-based approaches either lack a step-by-step, interpretable SQL generation process or fail to provide a universally applicable interaction design. To address these challenges, we introduce Interactive-T2S, a framework that generates SQL queries through direct interactions with databases. This framework includes four general tools that facilitate proactive and efficient information retrieval by the LLM. Additionally, we have developed detailed exemplars to demonstrate the step-wise reasoning processes within our framework. Our approach achieves advanced performance on the Spider and BIRD datasets as well as their variants. Notably, we obtain state-of-the-art results on the BIRD leaderboard under the setting without oracle knowledge, demonstrating the effectiveness of our method. Code and data are available at: https://github.com/JimXiongGM/Interactive-Text-to-SQL.
ESED: Emotion-Specific Evidence Decomposition for Uncertainty-Aware Multimodal Emotion Recognition in Conversation
Authors: Zechang Xiong, Zhenyan Ji, Wenkang Kong, Jiuqian Dai, Shen Yin
Venue: CIKM
Year: 2025

Multimodal emotion recognition in conversations is inherently challenging due to ambiguous cues, modality conflicts, and temporal dynamics, all of which contribute to complex and diverse uncertainty sources. While some recent methods incorporate uncertainty modeling, they often focus on overall prediction confidence, without explicitly distinguishing the different sources of uncertainty introduced by underlying factors. To address these challenges, we propose a novel Emotion-Specific Evidence Decomposition framework (ESED) that leverages evidential deep learning to explicitly model and disentangle multimodal emotional uncertainty. Rather than directly fusing features, ESED decomposes each modality's evidence into three interpretable components: (1) emotion-consistent evidence, capturing shared emotional cues across modalities; (2) emotion-specific evidence, highlighting the unique emotional role of each modality; and (3) dynamic evidence, modeling utterance-level temporal variations. These components are adaptively weighted based on emotional intensity, ambiguity, and dynamicity, quantified via prediction entropy, inter-modal divergence, and temporal variance. The final prediction is obtained through an adaptive fusion of these weighted components. Extensive experiments demonstrate that ESED outperforms the state-of-the-art methods on the MELD and IEMOCAP datasets, demonstrating the effectiveness of our proposed method.
Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search
Authors: Zeyu Xiong, Yixuan Nan, Li Gao, Hengzhu Tang, Shuaiqiang Wang, Junfeng Wang, Dawei Yin
Venue: CIKM
Year: 2025

In the dynamic landscape of large-scale web search, Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query, which is essential for improving user engagement and facilitating rapid decision-making. Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications. However, these approaches suffer from two key limitations: 1) The multi-stage pipeline often introduces cumulative information loss and architectural bottlenecks due to its weakest component; 2) Traditional models lack sufficient semantic understanding of both user queries and documents, particularly when dealing with complex search intents. In this study, we propose a novel framework to pioneer the application of generative models to address real-time QDTS in industrial web search. Our approach integrates large model distillation, supervised fine-tuning, direct preference optimization, and lookahead decoding to transform a lightweight model with only 0.1B parameters into a domain-specialized QDTS expert. Evaluated on multiple industry-relevant metrics, our model outperforms the production baseline and achieves a new state of the art. Furthermore, it demonstrates excellent deployment efficiency, requiring only 334 NVIDIA L20 GPUs to handle ~50,000 queries per second under 55~ms average latency per query.
Interactive Text-to-Visualization: Refining Visualization Outputs Through Natural Language User Feedback
Authors: Xubang Xiong, Raymond Chi-Wing Wong, Yuanfeng Song
Venue: CIKM
Year: 2025

Data visualization (DV) is of significance to the data analysis applications, exploring the hidden patterns and showing the insightful data. The task of Text-to-Vis, which takes text as input and generates data visualizations, was proposed to lower the threshold for generating DVs. However, the existing methods only view this problem as a one-shot mapping problem, directly outputting the final DVs without considering any user feedback for refining the generated DVs. Motivated by this, a more interactive scenario is investigated, where users could provide natural language feedback to refine the generated DVs. The scenario is formulated as the Text-to-Vis with Feedback problem. A new dataset is also created to further study the problem, which contains the user utterance, database schema, generated DVs, the natural language feedback and the refined DVs. A large language model (LLM) based framework named Vis-Edit is designed for handling this task, including schema linking, clause location, clause generation, merger and self-consistency. Eventually, extensive experiments reveal the effectiveness of Vis-Edit.
Exploring the Potential of Pre-Trained Language Models in Long-Term Semantic Scene Change Prediction Using Variable Scene Graphs
Authors: Haoyi Xiu, Xin Liu, Taehoon Kim, Kyoung-Sook Kim
Venue: CIKM
Year: 2025

The 3D Variable Scene Graph (3DVSG) is a newly emerging representation for modeling dynamic environments, extending scene graphs by introducing a node-level property called variability, which quantifies the likelihood of semantic change over time. In this work, we explore the integration of pre-trained language models (PLMs) into variability estimation. This is of significant practical importance because variability estimation suffers from data scarcity and severe class imbalance. PLMs provide a rich general semantic knowledge that can enhance representation learning in such settings. We systematically evaluate PLM embeddings across different graph neural networks (GNNs). We introduce a template-based text structuring (TTS) to understand the effect of input formatting. Our experiments show that PLM embeddings significantly improve variability estimation performance, with effectiveness influenced by both embedding and GNN choices. Also, we demonstrate that text structure can significantly affect embedding quality. Lastly, we demonstrate that PLM embeddings yield reliable gains in variability estimation and downstream active change detection.
Multi-Behavior Intent Disentanglement for Recommendation via Information Bottleneck Principle
Authors: Tongxin Xu, Chenzhong Bin, Cihan Xiao, Yunhui Li, Tianlong Gu
Venue: CIKM
Year: 2025

In e-commerce, recommender systems help users find suitable products by leveraging diverse behaviors, e.g., view, cart and buy. In recent years, multi-behavior recommender systems have made strides by integrating auxiliary behaviors with purchase histories to deliver high-quality recommendations. However, most existing methods often fail to identify spurious correlation intents within auxiliary behaviors that conflict with users' target intents. Indiscriminately incorporating such correlations into the prediction of target intents may lead to performance degradation. Toward this end, we propose a Multi-Behavior Intent Disentanglement (MBID) framework based on Information Bottleneck (IB) principle, which focuses on disentangling spurious correlation intents in multi-behavior recommendations. In particular, we design a projection-based intent extraction method to decompose the genuine and spurious correlation intents in auxiliary behaviors. Building on this, we conceive an IB-based multi-intent learning task to disentangle the spurious correlation intents and transfer the genuine correlation intents from auxiliary behaviors into the target behavior, yielding high-quality target intent representations. Experiments on three real-world datasets show MBID significantly outperforms the state-of-the-art baselines by effectively disentangling the spurious correlation intents.
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
Authors: Xiongxiao Xu, Canyu Chen, Yueqing Liang, Baixiang Huang, Guangji Bai, Liang Zhao, Kai Shu
Venue: CIKM
Year: 2025

Time series forecasting has made significant advances, including with Transformer-based models. The attention mechanism in Transformer effectively captures temporal dependencies by attending to all past inputs simultaneously. However, its quadratic computational complexity with respect to sequence length limits the scalability for long-range modeling. Recent state space models (SSMs) such as Mamba offer a promising alternative by achieving linear complexity without attention. Yet, Mamba compresses historical information into a fixed-size latent state, potentially causing information loss and limiting representational effectiveness. This raises a key research question: Can we design a hybrid Mamba-Transformer architecture that is both effective and efficient for time series forecasting? To address it, we adapt a hybrid Mamba-Transformer architecture Mambaformer, originally proposed for language modeling, to the time series domain. Preliminary experiments reveal that naively stacking Mamba and Transformer layers in Mambaformer is suboptimal for time series forecasting, due to an information interference problem. To mitigate this issue, we introduce a new time series decomposition strategy that separates time series into long-range patterns and short-range variations. Then we show that Mamba excels at capturing long-term structures, while Transformer is more effective at modeling short-term dynamics. Building on this insight, we propose State Space Transformer (SST), a multi-scale hybrid model with expert modules: a Mamba expert for long-range patterns and a Transformer expert for short-term variations. To facilitate learning the patterns and variations, SST employs a multi-scale patching mechanism to adaptively adjust time series resolution: low resolution for long-term patterns and high resolution for short-term variations. Comprehensive experiments on real-world datasets demonstrate that SST achieves state-of-the-art performance while scaling linearly with sequence length (O(L)). The code is available on GitHub.
In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
Authors: Shangqing Xu, Harshavardhan Kamarthi, Haoxin Liu, B. Aditya Prakash
Venue: CIKM
Year: 2025

Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-trained to enhance performance on specific tasks and often struggle to generalize to unseen tasks without fine-tuning. To address this limitation, we propose augmenting TSFMs with In-Context Learning (ICL) capabilities, enabling them to perform test-time inference by dynamically adapting to input-output relationships provided within the context. Our framework, In-Context Time-series Pre-training (ICTP), restructures the original pre-training data to equip the backbone TSFM with ICL capabilities, enabling adaptation to unseen tasks. Experiments demonstrate that ICT improves the performance of state-of-the-art TSFMs by approximately 11.4% on unseen tasks without requiring fine-tuning.
Improving Recommendation Fairness via Graph Structure and Representation Augmentation
Authors: Tongxin Xu, Wenqiang Liu, Chenzhong Bin, Cihan Xiao, Zhixin Zeng, Tianlong Gu
Venue: CIKM
Year: 2025

Graph Convolutional Networks (GCNs) have become increasingly popular in recommendation systems. However, recent studies have shown that GCN-based models will cause sensitive information to disseminate widely in the graph structure, amplifying data bias and raising fairness concerns. While various fairness methods have been proposed, most of them neglect the impact of biased data on representation learning, which results in limited fairness improvement. Moreover, some studies have focused on constructing fair and balanced data distributions through data augmentation, but these methods significantly reduce utility due to disruption of user preferences. In this paper, we aim to design a fair recommendation method from the perspective of data augmentation to improve fairness while preserving recommendation utility. To achieve fairness-aware data augmentation with minimal disruption to user preferences, we propose two prior hypotheses. The first hypothesis identifies sensitive interactions by comparing outcomes of performance-oriented and fairness-aware recommendations, while the second one focuses on detecting sensitive features by analyzing feature similarities between biased and debiased representations. Then, we propose a dual data augmentation framework for fair recommendation, which includes two data augmentation strategies to generate fair augmented graphs and feature representations. Furthermore, we introduce a debiasing learning method that minimizes the dependence between the learned representations and sensitive information to eliminate bias. Extensive experiments on two real-world datasets demonstrate the superiority of our proposed framework.
KALE: Knowledge Aggregation for Label-free Model Enhancement
Authors: Yuebin Xu, Xuemei Peng, Zhiyi Chen, Zeyi Wen
Venue: CIKM
Year: 2025

Large foundation models have demonstrated remarkable success in natural language processing and computer vision. Applying the large models to downstream tasks often requires fine-tuning, in order to boost the predictive accuracy. However, the fine-tuning process relies heavily on labeled data and extensive training. This dependency makes fine-tuning impractical for niche applications, such as rare object detection or specialized medical tasks. To overcome these limitations, we propose KALE: Knowledge Aggregation for Label-free model Enhancement, a label-free method for model enhancement, leveraging knowledge aggregation via model fusion and adaptive representation alignment. Our method is powered by a carefully designed joint self-cooperative optimization function that considers (i) multi-granularity optimization (task-specific and layer-specific), (ii) self and cooperative supervision integration, and (iii) mitigation of error accumulation caused by entropy minimization. Additionally, we introduce a class cardinality-aware sample filtering to ensure the stability of the fusion process. We also design a lightweight representation alignment technique to refine the fusion coefficient in a few shots for quality enhancement. We evaluate our method on multiple image classification datasets using ViT-B/32 and ViT-L/14 backbones. Experimental results demonstrate that our label-free method consistently outperforms state-of-the-art unsupervised approaches, including TURTLE and supervised full fine-tuning, in terms of average performance. Specifically, compared to TURTLE, our method achieves average improvements of 20.7% with ViT-B/32 and 19.5% with ViT-L/14. Furthermore, on the challenging SUN397 dataset, our method surpasses supervised full fine-tuning by 4% and 2.3% with ViT-B/32 and ViT-L/14, respectively.
LGC-CR: Few-shot Knowledge Graph Completion via Local Global Contrastive Learning and LLM-Guided Refinement
Authors: Yiming Xu, Qi Song, Yihan Wang, Wangqiu Zhou, Junli Liang
Venue: CIKM
Year: 2025

Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer novel query triples for few-shot relations from limited references. Despite promising progress, existing methods face two key challenges: (1) They often overlook rich higher-order neighbors, while traditional high-order aggregation methods are prone to introducing noise and lack effective alignment across multi-view neighborhood information. (2) Meta-learning methods over-rely on embeddings, making them susceptible to spurious relational patterns. Meanwhile, LLM-based methods, despite their potential, suffer from hallucinations and input constraints. To this end, we propose a novel framework that combines meta-learning, enhanced via a Local-Global Contrastive network, with LLM-guided Contextual Refinement (LGC-CR). At the data level, we design a local-global contrastive network to jointly aggregate relevant local features and capture stable global representations while filtering high-order noise, then align these two views through a dual contrast module to ensure consistency. At the model level, we employ an LLM refinement module, which retrieves relevant contexts to construct prompts and applies a knowledge selector to identify high-quality facts based on diversity and centrality, enabling efficient fine-tuning of LLMs to refine the preliminary predictions of meta-learning. The experimental results demonstrate that LGC-CR delivers better and more robust performance than state-of-the-art baselines, with Hit@1 improvements of 8.1%, 21.7%, and 20.6% on NELL, Wiki, and FB15K, respectively.
Reinforcement Learning-Driven Generative Retrieval with Semantic-aligned Multi-Layer Identifiers
Authors: Bo Xu, Yicen Tian, Xiaokun Zhang, Erchen Yu, Dailin Li, Linlin Zong, Hongfei Lin
Venue: CIKM
Year: 2025

Generative retrieval enhances retrieval effectiveness by generating natural language represented document identifiers. However, current methods often struggle with two major challenges: limited identifier quality and insufficient query-document interaction, leading to limited retrieval performance. To tackle these challenges, we propose a novel generative retrieval framework integrated with semantic-aligned multi-layer identifiers and reinforcement learning. To improve identifier quality, we design a prompt-driven multi-task learning strategy to generate three types of hierarchical identifiers: summary, keyword, and pseudo-query, to capture multi-granularity document semantics. Furthermore, we adopt supervised fine-tuning to integrate these identifiers. To improve query-document interaction, we devise a multi-view ranking fusion mechanism that combines retrieval results across multi-layer identifiers. We further employ a GRPO-based reinforcement learning based on dense similarity rewards and a difficulty-aware negative sampling strategy to optimize the generated identifiers. Experiments on multiple benchmark datasets show that our framework significantly outperforms existing generative retrieval methods, offering a promising solution for building more effective and semantically aligned retrieval systems. The code for our model is publicly available at https://github.com/yicentian02/GRAM-RL.