OpenRaiser/Intern-Atlas huggingface dataset:
KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking
Authors: Juyeon Kim, Geon Lee, Taeuk Kim, Kijung Shin
Venue: SIGIR
Year: 2025

Entity linking (EL) aligns textual mentions with their corresponding entities in a knowledge base, facilitating various applications such as semantic search and question answering. Recent advances in multimodal entity linking (MEL) have shown that combining text and images can reduce ambiguity and improve alignment accuracy. However, most existing MEL methods overlook the rich structural information available in the form of knowledge-graph (KG) triples. In this paper, we propose KGMEL, a novel framework that leverages KG triples to enhance MEL. Specifically, it operates in three stages: (1) Generation: Produces high-quality triples for each mention by employing vision-language models based on its text and images. (2) Retrieval: Learns joint mention-entity representations, via contrastive learning, that integrate text, images, and (generated or KG) triples to retrieve candidate entities for each mention. (3) Reranking: Refines the KG triples of the candidate entities and employs large language models to identify the best-matching entity for the mention. Extensive experiments on benchmark datasets demonstrate that KGMEL outperforms existing methods. Our code, datasets, and online appendix are available at: https://github.com/juyeonnn/KGMEL.
Conversational Argument Search Under Selective Exposure: Strategies for Balanced Perspective Access
Authors: Kyusik Kim, Jeongwoo Ryu, Dongseok Heo, Hyungwoo Song, Changhoon Oh, Bongwon Suh
Venue: SIGIR
Year: 2025

Conversational argument search systems influence how users access diverse perspectives but are prone to selective exposure. To address this, we propose two strategies: an interface-level multi-agent framework that structures perspective presentation and an interaction-level questioning strategy that encourages deeper engagement. We evaluate these strategies through a 2 x 2 factorial user study, examining their impact on selective exposure. Results show that the multi-agent setup facilitates broader perspective comparison, while agent-initiated questioning fosters deeper reflection; together, they promote more balanced argument access. Based on these findings, we discuss conversational search systems to mitigate selective exposure by implementing multi-agent interactions and questioning mechanisms.
Learning to Rank with Variable Result Presentation Lengths
Authors: Norman Knyazev, Harrie Oosterhuis
Venue: SIGIR
Year: 2025

Learning to Rank (LTR) methods generally assume that each document in a top-K ranking is presented in an equal format. However, previous work has shown that users' perceptions of relevance can be changed by varying presentations, i.e., allocating more vertical space to some documents to provide additional textual or image information. Furthermore, presentation length can also redirect attention, as users are more likely to notice longer presentations when scrolling through results. Deciding on the document presentation lengths in a fixed vertical space ranking is an important problem that has not been addressed by existing LTR methods. We address this gap by introducing the variable presentation length ranking task, where simultaneously the ordering of documents and their presentation length is decided. Despite being a generalization of standard ranking, we show that this setting brings significant new challenges: Firstly, the probability ranking principle no longer applies to this setting, and secondly, the problem cannot be divided into separate ordering and length selection tasks. We therefore propose VLPL - a new family of Plackett-Luce list-wise gradient estimation methods for the joint optimization of document ordering and lengths. Our semi-synthetic experiments show that VLPL can effectively balance the expected exposure and attractiveness of all documents, achieving the best performance across different ranking settings. Furthermore, we observe that even simple length-aware methods can achieve significant performance improvements over fixed-length models. Altogether, our theoretical and empirical results highlight the importance and difficulties of combining document presentation with LTR.
Wiki-TabNER: Integrating Named Entity Recognition into Wikipedia Tables
Authors: Aneta Koleva, Martin Ringsquandl, Ahmed Hatem, Thomas A. Runkler, Volker Tresp
Venue: SIGIR
Year: 2025

Interest in solving table interpretation tasks has grown over the years, yet it still relies on existing datasets that may be overly simplified. This is potentially reducing the effectiveness of the dataset for thorough evaluation and failing to accurately represent tables as they appear in the real-world. To enrich the existing benchmark datasets, we extract and annotate a new, more challenging dataset. The proposed Wiki-TabNER dataset features complex tables containing several entities per cell, with named entities labeled using DBpedia classes. This dataset is specifically designed to address named entity recognition (NER) task within tables, but it can also be used as a more challenging dataset for evaluating the entity linking task. In this paper we describe the distinguishing features of the Wiki-TabNER dataset and the labeling process. In addition, we propose a prompting framework for evaluating the new large language models on the within tables NER task. Finally, we perform qualitative analysis to gain insights into the challenges encountered by the models and to understand the limitations of the proposed~dataset.
6th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2025)
Authors: Ralf Krestel, Hidir Aras, Linda Andersson, Florina Piroi, Allan Hanbury, Dean Alderucci
Venue: SIGIR
Year: 2025

Information retrieval systems for the patent domain have a long and evolving history, serving as effective tools to support patent experts in a variety of daily tasks. They facilitate patent landscape analysis, help in the drafting and evaluation tasks in the patenting process, and enable efficient information extraction to gain practical insights into new technologies and innovations. Moreover, they assist in identifying existing solutions, knowledge gaps, trends, and persistent challenges within specific technological fields, thereby informing strategic decision-making and innovation management. Advances in machine learning and natural language processing allow to further automate such tasks, e.g. paragraph retrieval, question answering (QA) or patent text generation. The exploration of semantic technologies for the intellectual property (IP) industry is still in its early stages, with significant potential yet to be unlocked. Investigating the use of artificial intelligence (AI) methods for the patent domain is therefore not only of academic interest, but also highly relevant for practitioners. Compared to other domains, high quality, semi-structured, annotated data is available in large volumes (a requirement for supervised machine learning models), making training large models easier. On the other hand, domain-specific challenges arise, such as very technical language or legal requirements for patent documents, and data from various disciplines and technological areas. With the 6th edition of this workshop we will provide a platform for researchers and industry to discuss recent developments for semantic patent retrieval and analysis employing sophisticated methods ranging from patent text mining, domain-specific information retrieval to large language models (LLMs) targeting next generation applications and use cases for the IP and related domains.
Evaluating Contrastive Feedback for Effective User Simulations
Authors: Andreas Konstantin Kruff, Timo Breuer, Philipp Schaer
Venue: SIGIR
Year: 2025

The use of Large Language Models (LLMs) for simulating user behavior in the domain of Interactive Information Retrieval has recently gained significant popularity. However, their application and capabilities remain highly debated and understudied. This study explores whether the underlying principles of contrastive training techniques, which have been effective for fine-tuning LLMs, can also be applied beneficially in the area of prompt engineering for user simulations. Previous research has shown that LLMs possess comprehensive world knowledge, which can be leveraged to provide accurate estimates of relevant documents. This study attempts to simulate a knowledge state by enhancing the model with additional implicit contextual information gained during the simulation. This approach enables the model to refine the scope of desired documents further. The primary objective of this study is to analyze how different modalities of contextual information influence the effectiveness of user simulations. Various user configurations were tested, where models are provided with summaries of already judged relevant, irrelevant, or both types of documents in a contrastive manner. The focus of this study is the assessment of the impact of the prompting techniques on the simulated user agent performance. We hereby lay the foundations for leveraging LLMs as part of more realistic simulated users.
DAR: Dimension-Adaptive Recommendation with Multi-Granular Noise Control
Authors: Riwei Lai, Li Chen, Rui Chen, Chi Zhang
Venue: SIGIR
Year: 2025

Implicit feedback has become the primary source of training data for modern recommender systems due to its abundance and ease of collection. However, the inherent noise in implicit feedback poses significant challenges to model training. Existing denoising approaches either completely remove suspected noisy interactions (re-sampling) or uniformly adjust their importance (re-weighting). Such coarse-grained treatments fail to capture the complex nature of noise in real-world scenarios, where different aspects of an interaction may have varying noise levels. In this paper, we propose a novel perspective on fine-grained denoising. Our key insight is that noisy interactions often result from simple, impulsive decisions triggered by surface-level aspects and thus require less information to represent, while genuine preferences involve more complex considerations of aspects that need richer representations. Based on this insight, we propose DAR, a dimension-adaptive recommendation framework that dynamically adjusts each interaction's representation dimension to achieve fine-grained denoising control (re-scaling). Grounded on the Information Bottleneck theory, we establish that this dimension-adaptive approach provides a principled solution for preserving useful signals while limiting noise propagation. DAR implements this theoretical insight through three key components: (1) multi-granular noise estimation that considers interaction-level, user-level, and item-level signals (2) dimension adaptation that determines appropriate representation dimensions based on estimated noise levels, and (3) temporal smoothing that ensures stable training. Extensive experiments on real-world datasets demonstrate that DAR consistently outperforms state-of-the-art denoising methods, while providing interpretable insights about interaction noise. The source code is publicly available at https://github.com/Riwei-HEU/DAR.
GINGER: Grounded Information Nugget-Based Generation of Responses
Authors: Weronika Lajewska, Krisztian Balog
Venue: SIGIR
Year: 2025

Retrieval-augmented generation (RAG) faces challenges related to factual correctness, source attribution, and response completeness. To address them, we propose a modular pipeline for grounded response generation that operates on information nuggets - minimal, atomic units of relevant information extracted from retrieved documents. The multistage pipeline encompasses nugget detection, clustering, ranking, top cluster summarization, and fluency enhancement. It guarantees grounding in specific facts, facilitates source attribution, and ensures maximum information inclusion within length constraints. Experiments on the TREC RAG'24 dataset, using the AutoNuggetizer framework, demonstrate that GINGER achieves state-of-the-art performance on this benchmark.
Boosting Discriminability for Robust Multimodal Entity Linking with Visual Modality Missing
Authors: Mingrui Lao, Zheng Li, Yanming Guo, Xueyi Zhang, Siqi Cai, Zhaoyun Ding, Haizhou Li
Venue: SIGIR
Year: 2025

Multimodal Entity Linking (MEL) aims to retrieve ambiguous mentions within multimodal contexts to the referent entities in a multimodal knowledge base, typically based on the assumption of modality completeness. However, when deployed in open-world applications, MEL systems may encounter uncertainly missing of visual modalities from user-proposed mentions. In this paper, we propose a novel setting dubbed MEL-MM to simulate the practical challenge, and reveal that the semantic discriminability is a crucial factor to enhance the anti-missingness resilience. To this end, we introduce an innovative yet efficient approach termed Cross-View Introspective Ranking Distillation (CVIRD), which seeks to sufficiently align the linking similarities between teacher and student models trained from modality-complete and incomplete data. To be specific, as the first concept in CVIRD, Missing-Aware Ranking Distillation (MARD) focuses on modeling the discriminability by formulating the similarity rankings between mention and entities in a missing-sensitive and differentiable manner. Moreover, the second concept of Cross-View Distillation with Introspection (CVDI) aims to improve discriminability extraction in MARD through multi-level distillation, considering both cross-view retrieval and self-consistency. Experiments verify the effectiveness and model-agnostic ability of our method, which achieves superior performance in contrast to competitive missingness-resilient strategies.
A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
Authors: Kayhan Latifzadeh, Jacek Gwizdka, Luis A. Leiva
Venue: SIGIR
Year: 2025

We contribute a comprehensive dataset to study user attention and purchasing behavior on Search Engine Result Pages (SERPs). Previous work has relied on mouse movements as a low-cost large-scale behavioral proxy but also has relied on self-reported ground-truth labels, collected at post-task, which can be inaccurate and prone to biases. To address this limitation, we use an eye tracker to construct an objective ground-truth of continuous visual attention. Our dataset comprises 2,776 transactional queries on Google SERPs, collected from 47 participants, and includes: (1)~HTML source files, with CSS and images; (2)~rendered SERP screenshots; (3)~eye movement data; (4)~mouse movement data; (5)~bounding boxes of direct display and organic advertisements; and (6)~scripts for further preprocessing the data. In this paper we provide an overview of the dataset and baseline experiments (classification tasks) that can inspire researchers about the different possibilities for future work.
A Reproducibility Study of LLM Setwise Reranker with Heapsort
Authors: Dawn J. Lawrie, Efsun Selin Kayi, James Mayfield, Eugene Yang, Andrew Yates, Douglas W. Oard
Venue: SIGIR
Year: 2025

Large language models (LLMs) can be effective at retrieval but are generally too expensive to use as first-stage rankers. As a consequence, several approaches to their use as rerankers of less expensive first-stage retrieval results have been suggested. Zhuang et al. recommend setwise approaches. In particular, they use heapsort to efficiently return top-ranked documents by obtaining a partial order from each LLM call. Utilizing rerankers requires setting parameters such as %window size, number of input documents, number of documents to be reranked, evaluation depth, number of tokens per document, and selection of LLM, all of which contribute to latency and effectiveness. In this work, we reproduce the batching and reranking of Zhuang et al. with a larger comparison window size. Furthermore, we determine that the document truncation used in the original implementation is suboptimal. By providing more context to the LLM, we show that the reranker is more effective than originally reported.
Generate-Distill: Training Cross-Language IR Models with Synthetically-Generated Data
Authors: Dawn J. Lawrie, Efsun Selin Kayi, Eugene Yang, James Mayfield, Douglas W. Oard, Scott Miller
Venue: SIGIR
Year: 2025

Most pretrained language models that support neural information retrieval are fine-tuned on the MS MARCO dataset. MS MARCO is expressed in English, so it naturally supports monolingual English retrieval. However, for Cross-Language Information Retrieval (CLIR), no similar training data naturally exists that matches the languages of the query and the documents. The main ways to address this problem have been to continue to fine-tune with English data, or to translate MS MARCO queries and/or documents to match the CLIR setting. Machine translation often introduces errors that reduce retrieval effectiveness. It is usually easy to find target language documents suitable for training, but difficult to find naturally-occurring queries in the query language. An alternative is to train on naturally-occurring documents and synthetically-generated queries. Generate-Distill uses this approach with state-of-the-art distillation methods to match the effectiveness of training with translated MS~MARCO across different domains.
IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval
Authors: Youngjune Lee, Haeyu Jeong, Changgeon Lim, Jeong Choi, Hongjun Lim, Hangon Kim, Jiyoon Kwon, Saehun Kim
Venue: SIGIR
Year: 2025

Online community platforms require dynamic personalized retrieval and recommendation that can continuously adapt to evolving user interests and new documents. However, optimizing models to handle such changes in real-time remains a major challenge in large-scale industrial settings. To address this, we propose the Interest-aware Representation and Alignment (IRA) framework, an efficient and scalable approach that dynamically adapts to new interactions through a cumulative structure. IRA leverages two key mechanisms: (1) Interest Units that capture diverse user interests as contextual texts, while reinforcing or fading over time through cumulative updates, and (2) a retrieval process that measures the relevance between Interest Units and documents based solely on semantic relationships, eliminating dependence on click signals to mitigate temporal biases. By integrating cumulative Interest Unit updates with the retrieval process, IRA continuously adapts to evolving user preferences, ensuring robust and fine-grained personalization without being constrained by past training distributions. We validate the effectiveness of IRA through extensive experiments on real-world datasets, including its deployment in the Home Section of NAVER's CAFE, South Korea's leading community platform.
SEALR: Sequential Emotion-Aware LLM-Based Personalized Recommendation System
Authors: Namjun Lee, Jaekwang Kim
Venue: SIGIR
Year: 2025

Large Language Models (LLMs) excel in various NLP tasks but remain underexplored in recommendation systems. This study proposes the Sequential Emotion-Aware LLM-Based Personalized Recommendation System (SEALR ) to leverage sentiment analysis in user-generated reviews, tracking emotional changes and extracting sentiment labels. It integrates candidate items produced by sequential models with user behavior data into an LLM, enhancing personalization. Experiments on Amazon and Yelp datasets explore the effect of varied candidate pool sizes and instruction-based fine-tuning ratios, demonstrating significant performance gains. The combination of sentiment insights and user behavior data effectively accommodates diverse user preferences and contexts.
RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
Authors: Santiago de Leon-Martinez, Jingwei Kang, Róbert Móro, Maarten de Rijke, Branislav Kveton, Harrie Oosterhuis, Mária Bieliková
Venue: SIGIR
Year: 2025

Carousel interfaces are widely used in e-commerce and streaming services, but little research has been devoted to them. Previous studies of interfaces for presenting search and recommendation results have focused on single ranked lists, but it appears their results cannot be extrapolated to carousels due to the added complexity. Eye tracking is a highly informative approach to understanding how users click, yet there are no eye tracking studies concerning carousels. There are very few interaction datasets on recommenders with carousel interfaces and none that contain gaze data. We introduce the RecGaze dataset: the first comprehensive feedback dataset on carousels that includes eye tracking results, clicks, cursor movements, and selection explanations. The dataset comprises of interactions from 3 movie selection tasks with 40 different carousel interfaces per user. In total, 87 users and 3,477 interactions are logged. In addition to the dataset, its description and possible use cases, we provide results of a survey on carousel design and the first analysis of gaze data on carousels, which reveals a golden triangle or F-pattern browsing behavior. Our work seeks to advance the field of carousel interfaces by providing the first dataset with eye tracking results on carousels. In this manner, we provide and encourage an empirical understanding of interactions with carousel interfaces, for building better recommender systems through gaze information, and also encourage the development of gaze-based recommenders.
Seeing Beyond Hallucinations: LLM-based Compositional Information Extraction for Multimodal Reasoning
Authors: Xinwei Li, Li Lin, Shuai Wang, Hanqian Wu
Venue: SIGIR
Year: 2025

Advancements in Multimodal Large Language Models (MLLMs) have significantly improved information extraction and retrieval performance. Despite these achievements, MLLMs still suffer from the visual object hallucination problem, where models produce plausible, yet incorrect, or irrelevant content not present in the input data. This issue arises from an over-reliance on ''bag-of-objects'' representations and language priors, leading to inadequate extraction of visual objects, along with their attributes and relationships. Existing methods to mitigate these hallucinations are limited by the significant human labor required and the coarse-grained nature. To overcome these challenges, we introduce Multimodal Contrastive Decoding (MMCD), a novel decoding approach that integrates graph-structured reasoning paths with contrastive decoding. MMCD mitigates object hallucinations induced by language priors and enhances the ability of MLLMs to extract and understand compositional information, without additional training or the usage of external tools. This is achieved by masking key objects in images, constructing perturbed scene graphs of attributes and relationships, then contrasting these with the original image and scene graph. Extensive evaluation across three distinct multimodal compositional reasoning tasks: spatial relationship reasoning, alignment of synthetic image and caption, and fine-grained object attribute understanding, show that MMCD consistently surpasses existing decoding methods when applied to various MLLMs. Moreover, MMCD achieves state-of-the-art performance on multiple benchmarks, including the What's Up, SeeTrue and SugarCrepe datasets.