OpenRaiser/Intern-Atlas huggingface dataset:
Continual Origin Tracing of LLM-Generated Text
Authors: Haoran Li, Quan Wang
Venue: SIGIR
Year: 2025

The rapid development of large language models (LLMs) raises concerns about their potential misuse. Accurately identifying and tracing the origin of LLM-generated content is crucial for accountability and transparency. Previous methods typically frame origin tracing as multi-class classification with a fixed label set, thus struggle to adapt to new LLMs without frequent retraining. This paper introduces a new task, continual origin tracing of LLM-generated text, which frames origin tracing in a continual learning or, more precisely, class-incremental learning manner, where new LLMs continuously emerge, and a model incrementally learns to identify new LLMs without forgetting old ones. A novel training-free method is further devised for the task, which continually extracts prototypes for emerging LLMs using a frozen pre-trained model, and conducts global and local prototype decorrelation to improve prototype matching, thus favoring more accurate tracing. To facilitate evaluation on the new task, we construct a benchmark comprising text generated by 19 recently released LLMs from 12 vendors that simulates a real-world scenario where these LLMs emerge over time and need to be recognized incrementally across 8 diverse domains. Rigorous evaluations on this benchmark highlight the effectiveness and potential of the proposed method in the new task, offering a promising direction for future research.
Hypercomplex Knowledge Graph-Aware Recommendation
Authors: Anchen Li, Bo Yang, Huan Huo, Farookh Hussain, Guandong Xu
Venue: SIGIR
Year: 2025

Knowledge graphs (KGs) consist of well-organized external information and have been proven to enhance recommendation quality effectively. Most KG-aware recommender systems are developed using real number space embeddings. In recent years, learning representations in the hypercomplex space has gained success and attention. Compared to single-component real-valued vectors, multi-component hypercomplex embeddings offer greater expressiveness, facilitating more meaningful modeling of users, items, entities, and their relations in the user-item interaction graph and KG. In this paper, we explore the integration of hypercomplex algebras in KG-aware recommendation and propose a Hypercomplex Knowledge Graph-aware Recommender (HKGR) method. Our HKGR models the interaction graph and KG in the hypercomplex space by utilizing specially designed hypercomplex graph neural networks. In particular, HKGR employs a hypercomplex attention-based aggregator to capture the structure and semantics of the KG. In the recommendation prediction phase, we design a hypercomplex interaction network that can approximate the high-order component interactions between users and items. Furthermore, we introduce a hypercomplex contrastive learning operator to strengthen cooperative signals between the interaction graph and KG modelings. Experiment results on the four real-world datasets show that our HKGR outperforms the state-of-the-art recommender baselines.
Incorporating Communication Style and Interaction of Speakers for Sarcasm Explanation in Dialogue
Authors: Yuqing Li, Wenyuan Zhang, Zheng Lin, Guoxuan Ding, Weiping Wang
Venue: SIGIR
Year: 2025

Sarcasm Explanation in Dialogue (SED) task aims to uncover the underlying meaning of sarcastic expressions in multimodal dialogues. While previous studies have largely focused on modeling dialogue content, they often neglect the influence of speakers and the interactions between utterances. To address this gap, we propose a novel framework called CISI, which integrates personalized communication styles, inter-speaker interaction relationships, and sarcasm-centric multimodal cues to enhance SED. To capture how personalized styles influence sarcasm expression, we model speakers' communication styles using Satir's Communication Model in psychology. Furthermore, we model the flow of sarcasm through discourse parsing, constructing explicit conversational interaction and dependencies between speakers. Lastly, we design a multimodal fusion module that aligns modality-specific cues with sarcasm-related semantics to enhance understanding. Extensive experiments on the WITS dataset demonstrate that CISI achieves superior performance. We also obtain competitive results on the MUStARD dataset for dialogue-level multimodal sarcasm detection, further showcasing the generalizability of CISI.
CD-CDR: Conditional Diffusion-based Item Generation for Cross-Domain Recommendation
Authors: Hanyu Li, Jiayu Li, Weizhi Ma, Peijie Sun, Haiyang Wu, Jingwen Wang, Yuekui Yang, Min Zhang, Shaoping Ma
Venue: SIGIR
Year: 2025

Cross-domain recommendation (CDR) has emerged as a promising direction for expanding the applicability of recommendation systems. Recent advances in CDR have demonstrated the effectiveness of the unified distribution paradigm, which leverages shared distributions to transfer knowledge across domains and employs domain-specific adapters for targeted recommendations. While this well-designed paradigm shows promising performance, existing methods require extra supervision signals (e.g. contrastive learning on domain-masked embeddings) to maintain unified distributions across domains, leading to an inherent trade-off between unified objectives and domain-specific preference modeling. To address these limitations, we propose CD-CDR (Conditional Diffusion-CDR), a novel approach that leverages a shared conditional diffusion model to learn unified item distributions and facilitate knowledge transfer across domains. The key insight is to utilize the powerful generative capabilities of diffusion models to learn a shared distribution while naturally incorporating domain-specific characteristics through conditional generation. This design enables CD-CDR to replace traditional adapters with generation conditions as an integral part of the distribution model, thereby eliminating extra supervision signals and fundamentally resolving the trade-off between unified and domain-specific objectives. Extensive experiments on six domain pairs from two real-world datasets demonstrate that CD-CDR significantly outperforms existing methods for both normal and cold-start settings. To the best of our knowledge, this is the first work to explore the unified distribution paradigm in CDR using conditional diffusion models.
Long Context vs. RAG: Strategies for Processing Long Documents in LLMs
Authors: Xinze Li, Yushi Bai, Bowen Jin, Fengbin Zhu, Liangming Pan, Yixin Cao
Venue: SIGIR
Year: 2025

Large Language Models (LLMs) excel at zero- and few-shot learning but are restricted by the length of context windows when processing long documents. Two strategies have emerged to overcome this limitation: (1) Long Context (LC) methods, which extend or compress transformer architectures to input more text; and (2) Retrieval-Augmented Generation (RAG), which integrates external knowledge sources via embedding- or index-based retrieval. This half-day tutorial offers a unified, beginner-friendly introduction to both approaches. We first review transformer fundamentals-positional encoding, attention complexity, and common LC techniques. Next, we explain the classic RAG pipeline and recent RAG strategies, alongside evaluation metrics and benchmarks. We also analyze recent empirical studies to highlight strengths, limitations, and trade-offs of LC vs. RAG in terms of scalability, computational cost, and retrieval effectiveness. We conclude with best practices for real-world deployments, emerging hybrid architectures, and open research directions, equipping IR researchers and practitioners with actionable guidelines for processing long documents in LLMs.
Graph-Augmented Retrieval with Memory-Driven Reasoning and Constraint-Aware Filtering for MultiHop QA
Authors: Siyuan Li, Yongping Du, Mingyang Li
Venue: SIGIR
Year: 2025

Addressing multi-hop reasoning of complex query effectively is a challenging task in information retrieval field. It demands the ability to retrieve and integrate dispersed knowledge across multiple documents dynamically while maintaining coherence in multi-step reasoning process. This study addresses these challenges with three primary contributions. It explores the integrating of large language models with graph-augmented retrieval methods for complex multihop reasoning. Moreover, the Memory-Driven Chain-of-Reasoning strategy is introduced, leveraging the memory of historical queries and results to optimize multi-step reasoning dynamically. Additionally, the Constraint-Aware Filtering in Chunked Window strategy is developed to improve retrieval precision by partitioning and filtering large retrieval windows based on query constraints. The experiments on public benchmarks indicate that our method substantially outperforms competitive approaches, achieving up to 13.8% and 14.0% improvements in EM and F1 on HotpotQA, 9.4% and 12.8% on MuSiQue, and 6.4% and 3.2% on 2WikiMultiHopQA, respectively.
Denoising Multi-Interest-Aware Logical Reasoning for Long-Sequence Recommendation
Authors: Fei Li, Qingyun Gao, Yizhou Dang, Enneng Yang, Guibing Guo, Jianzhe Zhao, Xingwei Wang
Venue: SIGIR
Year: 2025

Logical reasoning-based recommendation methods employ logical rules to mitigate the adverse effects of noise items in short interaction sequences on recommendation accuracy. However, there are two problems with existing methods: 1) As the length of the interaction sequence increases, introducing more noise items exacerbates the negative impact on logical reasoning, thereby reducing the accuracy of these methods. 2) They are often dominated by the user's single primary interest, which prevents simultaneous consideration of users' multiple-aspect interests in long sequences. To address these issues, we propose a novel dEnoising Multi-Interest-aware Logical rEasoning (EMILE) method for long-sequence recommendation. Specifically, we design a logical rule-based interest extractor that enhances the importance of preferred items in constructing user interests while minimizing the negative impact of disliked items. This extractor effectively mitigates the adverse effects of noise items in long interaction sequences. Furthermore, we propose a novel multi-interest learning strategy that optimizes two new objective functions-interest probability distribution contrastive loss and interest logical reasoning contrastive loss-to ensure the model simultaneously considers multiple-aspect interests. These two objective functions require that the target item is more closely aligned with multiple interests than the single primary interest, both in the probability distribution space and during logical reasoning. Experimental results on four public datasets demonstrate that our method significantly outperforms all compared baselines regarding recommendation accuracy. Code is available at https://github.com/muzi1998/Denoising-Multi-Interest-Aware-Logical-Reasoning.
Towards Principled Learning for Re-ranking in Recommender Systems
Authors: Qunwei Li, Linghui Li, Jianbin Lin, Wenliang Zhong
Venue: SIGIR
Year: 2025

As the final stage of recommender systems, re-ranking presents ordered item lists to users that best match their interests. It plays such a critical role and has become a trending research topic with much attention from both academia and industry. Recent advances of re-ranking are focused on attentive listwise modeling of interactions and mutual influences among items to be re-ranked. However, principles to guide the learning process of a re-ranker, and to measure the quality of the output of the re-ranker, have been always missing. In this paper, we study such principles to learn a good re-ranker. Two principles are proposed, including convergence consistency and adversarial consistency. These two principles can be applied in the learning of a generic re-ranker and improve its performance. We validate such a finding by various baseline methods over different datasets.
Are Information Retrieval Approaches Good at Harmonising Longitudinal Surveys in Social Science?
Authors: Wing Yan Li, Zeqiang Wang, Jon Johnson, Suparna De
Venue: SIGIR
Year: 2025

Automated detection of semantically equivalent questions in longitudinal social science surveys is crucial for long-term studies informing empirical research in the social, economic, and health sciences. Retrieving equivalent questions faces dual challenges: inconsistent representation of theoretical constructs (i.e. concept/sub-concept) across studies as well as between question and response options, and the evolution of vocabulary and structure in longitudinal text. To address these challenges, our multi-disciplinary collaboration of computer scientists and survey specialists presents a new information retrieval (IR) task of identifying concept (e.g. Housing, Job, etc.) equivalence across question and response options to harmonise longitudinal population studies. This paper investigates multiple unsupervised approaches on a survey dataset spanning 1946-2020, including probabilistic models, linear probing of language models, and pre-trained neural networks specialised for IR. We show that IR-specialised neural models achieve the highest overall performance with other approaches performing comparably. Additionally, the re-ranking of the probabilistic model's results with neural models only introduces modest improvements of 0.07 at most in F1-score. Qualitative post-hoc evaluation by survey specialists shows that models generally have a low sensitivity to questions with high lexical overlap, particularly in cases where sub-concepts are mismatched. Altogether, our analysis serves to further research on harmonising longitudinal studies in social science.
Heterogeneous Graph Embedding Made More Practical
Authors: Fangfang Li, Huihui Zhang, Wei Li, Wei Wu
Venue: SIGIR
Year: 2025

Heterogeneous graphs are prevalent in the real-world applications, and a key analytical task for such graphs is heterogeneous graph embedding, which seeks to represent each heterogeneous graph as a low-dimensional feature vector while preserving its inherent heterogeneity. Although the traditional methods have achieved significant advancements, they predominantly rely on modeling basic pairwise relationships between nodes, limiting their ability to capture the intricate structures and interactions present in heterogeneous graphs. Recent studies have begun incorporating simplicial complexes, which effectively encode higher-order interactions, into the Graph Neural Network (GNN) framework. However, these GNN-based approaches are computationally intensive due to the substantial parameter training involved. To address these challenges, we propose HGSketch, a practical heterogeneous graph embedding algorithm that balances performance and temporal efficiency without the powerful workhorses. By leveraging the Locality Sensitive Hashing (LSH) technique, HGSketch efficiently captures higher-order information from simplicial complexes locally and globally without the need for parameter learning. The extensive experiment results display that HGSketch achieves performance comparable to the state-of-the-art learning-based methods, while significantly reducing runtime by a factor of up to 1223.86; also, HGSketch generally outperforms the state-of-the-art LSH-based methods. We have released the source code and the datasets in https://github.com/AIandBD/graph-hashing/tree/main/HGSketch.
SMMR: Sampling-Based MMR Reranking for Faster, More Diverse, and Balanced Recommendations and Retrieval
Authors: Kiryl Liakhnovich, Oleg Lashinin, Andrei Babkin, Michael Pechatov, Marina Ananyeva
Venue: SIGIR
Year: 2025

Relevance and diversity are critical objectives in modern information retrieval (IR), particularly in recommender systems. Achieving a balance between relevance (exploitation) and diversity (exploration) optimizes user satisfaction and business goals such as catalog coverage and novelty. While existing post-processing reranking methods address this trade-off, they usually rely on greedy strategies, leading to suboptimal outcomes for large-scale tasks. To this end, we propose Sampled Maximal Marginal Relevance (SMMR), a novel sampling-based extension of MMR that introduces randomness into item selection to improve relevance-diversity trade-offs. SMMR avoids the rigidity of greedy and deterministic reranking, and achieves a logarithmic computational speedup, which allows it to scale on large candidate sets. Our evaluations on multiple real-world open-source datasets demonstrate that SMMR consistently outperforms existing state-of-the-art approaches, offering superior performance in balancing relevance and diversity. Our implementation of the proposed method is made available to support future research.
How Users Interact with Generative Information Retrieval Systems: A Study of User Behavior and Search Experience
Authors: Yidong Liang, Zhijing Wu, Fan Zhang, Dandan Song, Heyan Huang
Venue: SIGIR
Year: 2025

The development of LLM has facilitated the emergence of generative information retrieval (IR) systems, such as ''Bing Chat''. Generative IR systems return generated text with citations rather than a list of ranked search results. User studies on IR systems are essential for understanding users' interaction patterns, evaluating and optimizing systems, and improving search experience, particularly in the context of generative IR systems with novel conversational interfaces and responses. However, systematic investigations into user behavior and search experience on generative IR systems are notably lacking. To address this gap, we conducted a user study using Bing Chat to explore user behavior and feedback on generative IR systems. The participants were required to accomplish three types of tasks using Bing Chat. During the search process, we collected their various behavior (e.g., click, query reformulation) and explicit feedback (e.g., satisfaction, credibility, and success). Additionally, the same study was conducted on traditional IR systems Bing for comparison. Analyses of these data show that Bing Chat can reduce the user's search effort and lead to a better search experience without any decrease in credibility compared with Bing. We believe that this work provides valuable insight into the design and evaluation of generative information retrieval systems.
A Flexible User Study Platform for Generative Information Retrieval
Authors: Yidong Liang, Zhijing Wu, Yuchen He, Fengming Liang, Kexin Liu, Jiaxin Mao
Venue: SIGIR
Year: 2025

User behavior and experience are important for improving information retrieval (IR) systems. While much research has focused on traditional IR systems, few studies have systematically examined user behavior and search experience with emerging generative IR systems. A key reason for this gap is the lack of publicly available toolkits to record user behavior and feedback in generative IR systems. We developed a comprehensive platform to collect user behavior and feedback on the generative IR system. This platform consists of: 1) a generative IR system that supports both API-based and customized retrieval-augmented generation (RAG) methods, 2) a user interface that logs various user behavior, including prompts, clicks, mouse movements, and scrolling, and 3) an annotation website that allows users to provide feedback. We believe the proposed platform has the potential to streamline data collection for user studies on generative IR systems, paving the way for future research on how users engage with and interact with these systems.
Document Similarity Enhanced IPS Estimation for Unbiased Learning to Rank
Authors: Zeyan Liang, Graham McDonald, Iadh Ounis
Venue: SIGIR
Year: 2025

Learning to Rank (LTR) models learn from historical user interactions, such as user clicks. However, there is an inherent bias in the clicks of users due to position bias, i.e., users are more likely to click highly-ranked documents than low-ranked documents. To address this bias when training LTR models, many approaches from the literature re-weight the users' click data using Inverse Propensity Scoring (IPS). IPS re-weights the user's clicks proportionately to the position in the historical ranking that a document was placed when it was clicked since low-ranked documents are less likely to be seen by a user. In this paper, we argue that low-ranked documents that are similar to highly-ranked relevant documents are also likely to be relevant. Moreover, accounting for the similarity of low-ranked documents to highly ranked relevant documents when calculating IPS can more effectively mitigate the effects of position bias. Therefore, we propose an extension to IPS, called IPSsim, that takes into consideration the similarity of documents when estimating IPS. We evaluate our IPSsim estimator using two large publicly available LTR datasets under a number of simulated user click settings, and with different numbers of training clicks. Our experiments show that our IPSsim estimator is more effective than the existing IPS estimators for learning an unbiased LTR model, particularly in top-n settings when n >= 30. For example, when n = 50, our IPSsim estimator achieves a statistically significant ~3% improvement (p < 0.05) in terms of NDCG compared to the Doubly Robust estimator from the literature.
Embedding-based Retrieval in Multi-Modal Content Moderation
Authors: Hanzhong Liang, Jinghao Shi, Xiang Shen, Zixuan Wang, Vera Wen, Ardalan Mehrani, Zhiqian Chen, Yifan Wu, Zhixin Zhang
Venue: SIGIR
Year: 2025

Video understanding plays a fundamental role for content moderation on short video platforms, enabling the detection of inappropriate content. While classification remains the dominant approach for content moderation, it often struggles in scenarios requiring rapid and cost-efficient responses, such as trend adaptation and urgent escalations. To address this issue, we introduce an Embedding-Based Retrieval (EBR) method designed to complement traditional classification approaches. We first leverage a Supervised Contrastive Learning (SCL) framework to train a suite of foundation embedding models, including both single-modal and multi-modal architectures. Our models demonstrate superior performance over established contrastive learning methods such as CLIP and MoCo. Building on these embedding models, we design and implement the embedding-based retrieval system that integrates embedding generation and video retrieval to enable efficient and effective trend handling. Comprehensive offline experiments on 25 diverse emerging trends show that EBR improves ROC-AUC from 0.85 to 0.99 and PR-AUC from 0.35 to 0.95. Further online experiments reveal that EBR increases action rates by 10.32% and reduces operational costs by over 80%, while also enhancing interpretability and flexibility compared to classification-based solutions.