OpenRaiser/Intern-Atlas huggingface dataset:
Intersectional Hatred - An Application of Large Language Models to Detect Hate and Offensive Speech Targeted at Congressional Candidates in the 2024 U.S. Election
Authors: Müge Finkel, Dhanaraj Thakur, Steven Finkel, Firat Durusan, Erdem Yörük, Isik Topçu, Melih Can Yardi, Amanda Zaner, Jungmin Han
Venue: WWW
Year: 2025

In this paper we take an intersectional approach to the problem of understanding hate and offensive speech targeted at all candidates who ran for Congress in the 2024 U.S. elections. We used a series of language models to analyze posts on X for instances of hate and offensive speech. This was based on a dataset of over 800,000 posts on X collected between May 20 and August 23, 2024. We found that, on average, more than 1 in 5 tweets targeted at Asian-American and African- American women candidates contained offensive speech, a higher proportion than other candidates. We also found that, on average, African- American women candidates were four times more likely than others to be targeted with hate speech, three times as likely as white women, and more than 18 times as likely as white men. These findings support prior research that women of color political candidates are more likely to be targeted with online abuse, a pattern which has important implications for the quality of American democracy.
AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Security
Authors: Scott Freitas, Jovan Kalajdjieski, Amir Gharib, Robert McCann
Venue: WWW
Year: 2025

Security operation centers contend with a constant stream of security incidents, ranging from straightforward to highly complex. To address this, we developed Microsoft Copilot for Security Guided Response (CGR), an industry-scale ML architecture that guides security analysts across three key tasks—(1) investigation, providing historical context by identifying similar incidents; (2) triaging to ascertain the nature of the incident—whether it is a true positive, false positive, or benign positive; and (3) remediation, recommending tailored containment actions. CGR is integrated into the Microsoft Defender XDR product and deployed worldwide, generating millions of recommendations across thousands of customers. Our extensive evaluation, incorporating internal evaluation, collaboration with security experts, and customer feedback, demonstrates that CGR delivers high-quality recommendations across all three tasks. We provide a comprehensive overview of the CGR architecture, setting a precedent as the first cybersecurity company to openly discuss these capabilities in such depth. Additionally, we release GUIDE, the largest public collection of real-world security incidents, spanning 13M evidences across 1M incidents annotated with ground-truth triage labels by customer security analysts.
Breaking Accessibility Barriers: An Ontology Proxy with Failure Recovery and Time Travel Capabilities
Authors: Johannes Frey, Jenifer Tabita Ciuciu-Kiss, Natanael Arndt
Venue: WWW
Year: 2025

This paper introduces a novel concept and implementation of an ontology proxy designed to seamlessly enhance accessibility and reliability of the Web of Ontologies by addressing challenges such as link rot, evolution inconsistencies, and communication failures. The proxy features a time travel mode, powered by DBpedia Archivo, that provides access to archived and versioned snapshots of ontologies. This enables failure recovery and the emulation of a consistent state in time, supporting reproducible research and enhancing the FAIRness of ontologies and associated (meta)data in a plug-and-play manner. Initial evaluations show significant improvements in ontology retrieval success rates, underscoring the proxy's potential as a viable interface for breaking accessibility barriers.
POTS - A Polyparadigmatic Ontology Term Search with Fine-Grained Context Steering using Hyper-Level Vector Spaces
Authors: Johannes Frey, Lucas Ferraz, Marvin Hofer
Venue: WWW
Year: 2025

We present a novel microservice-based system, that facilitates a polyparadigmatic ontology term search (leveraging semantic search via vector embeddings, keyword search, and attribute filters). The search index strategy intends to preserve important semantic aspects of the ontological context of a term (selected attributes and term relationships) using structured search fields and multilevel vector spaces assembling hyper-level vector spaces. The flexible, yet simple query API allows fine-grained search requests based on a combination of fuzzy and exact filters. The architecture is based on a highly automatable and flexible Docker Compose setup strategy. While deploying the system for a local ontology is only one command away, the setup also allows ingesting a configurable subset of over 1,800 published ontologies in over 12,000 versions via DBpedia Archivo.
Price Stability and Improved Buyer Utility with Presentation Design: A Theoretical Study of the Amazon Buy Box
Authors: Ophir Friedler, Hu Fu, Anna R. Karlin, Ariana Tang
Venue: WWW
Year: 2025

Platforms design the form of presentation by which sellers are shown to the buyers. This design not only shapes the buyers' experience but also leads to different market equilibria or dynamics. One component in this design is through the platform's mediation of the search frictions experienced by the buyers for different sellers. We take a model of monopolistic competition and show that, on one hand, when all sellers have the same inspection costs, the market sees no stable price since the sellers always have incentives to undercut each other, and, on the other hand, the platform may stabilize the price by giving prominence to one seller chosen by a carefully designed mechanism. This calls to mind Amazon's Buy Box. We study natural mechanisms for choosing the prominent seller, characterize the range of equilibrium prices implementable by them, and find that in certain scenarios the buyers' surplus improves as the search friction increases.
The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Authors: Junchen Fu, Xuri Ge, Xin Xin, Haitao Yu, Yue Feng, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
Venue: WWW
Year: 2025

Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA, GPT, Mistral, and CLIP. These models have achieved remarkable performance across various tasks of multimodal information retrieval (MIR), including web search, cross-modal retrieval, and recommender systems, etc. However, due to their enormous parameter sizes, significant efficiency challenges emerge across training, deployment, and inference stages when adapting these models' representation for IR tasks. These challenges present substantial obstacles to the practical adaptation of foundation models for representation learning in information retrieval tasks. To address these pressing issues, we propose organizing the first EReL@MIR workshop at the Web Conference 2025, inviting participants to explore novel solutions, emerging problems, challenges, efficiency evaluation metrics and benchmarks. This workshop aims to provide a platform for both academic and industry researchers to engage in discussions, share insights, and foster collaboration toward achieving efficient and effective representation learning for multimodal information retrieval in the era of large foundation models.
Enhancing Data Annotation for Student Models: A Self-Training Approach with Large Language Models
Authors: Gilad Fuchs, Alexander Nus, Yotam Eshel, Bracha Shapira
Venue: WWW
Year: 2025

In this paper we introduce a methodology that utilizes Large Language Models (LLMs) for efficient data annotation which is essential for student models training. We specifically focus on classification tasks, assessing the impact of various annotation methods. We employ LLMs, such as GPT-4 and 7B parameter open-source models, as teacher models to facilitate the annotation process. We proceed to evaluate the efficiency of smaller, more deployment-efficient student models like BERT in production environments. We show two alternative methods. One using a cascade of LLMs distillation, starting from GPT-4 and following by 7B parameter LLM and BERT. And a second method, which shows the feasibility of annotating data without relying on proprietary model like GPT-4, by exclusively using a 7B parameter open-source model, leveraging self-training methods. This strategic use of smaller LLMs in conjunction with smaller student models presents an efficient and cost-effective solution for enhancing classification tasks, offering insights into the potential of utilizing large-scale language models in E-Commerce production settings.
Practical Fact Checking System for LLMs
Authors: Gilad Fuchs, Oded Zinman, Ido Ben-Shaul
Venue: WWW
Year: 2025

The use of Large Language Models (LLM) like ChatGPT in real-world product solutions is significantly limited by the well-known issue of hallucinations. Various methods exist in order to overcome this issue automatically, such as using a different LLM to provide feedback on the accuracy of the generated text or by examining the output consistency given multiple sampled responses. However, these approaches do not guarantee factual accuracy, which is crucial in many specialized domains. In order to enhance the factual correctness of generated text by LLM models, a combination of manual annotations and supportive tools are required. We suggest a practical fact checking system tailored specifically for LLMs which combines a hybrid approach (human and machine) to evaluate the correctness of the generated text. This is particularly vital in fields where hallucinations present significant challenges. We use proprietary LLMs, both directly and through Retrieval-Augmented Generation (RAG), to offer users informed feedback on potential hallucinations via a user-friendly interface. We apply our methodology to the task of generating aspect values for video games in listings from an E-commerce marketplace, demonstrating the utility of our approach.
Worst-Case-Optimal Joins on Graphs with Topological Relations
Authors: José Fuentes-Sepúlveda, Adrián Gómez-Brandón, Aidan Hogan, Ayleen Irribarra-Cortés, Gonzalo Navarro, Juan L. Reutter
Venue: WWW
Year: 2025

Spatial data play an important role in many applications built over knowledge graphs, and are frequently referenced in queries posed to public query services, such as that of Wikidata. Querying for spatial data presents a significant challenge, as topological relations such as adjacent or contains imply inferred information, such as through the transitivity of the containment relation. However, despite all the recent advances in querying knowledge graphs, we still lack techniques specifically tailored for topological information. Applications looking to incorporate topological relations must either materialize the inferred relations, incurring high space and maintenance overheads, or query them with less efficient recursive algorithms, incurring high runtime overheads. In this paper we address the problem of leveraging topological information in knowledge graphs by designing efficient algorithms to process these queries. Our solution involves building a specific index that stores the topological information in a convenient compact form, and includes specialized algorithms that infer every possible relation from the basic topological facts in the graph. We show that, while using essentially the same space required to solve standard graph pattern queries, we can incorporate topological predicates, accounting for all the inferred information, all within worst-case-optimal time. We implement our scheme and show experimentally that it outperforms baseline solutions by a notable margin.
Graph-based Key Opinion Analyzer: Multifaceted Evaluation of Critical Opinions and Omission Detection in Summarization
Authors: Tomoki Fukuma, Koki Noda, Toshihide Ubukata, Kousuke Hosoi, Yoshiharu Ichikawa, Kyosuke Kambe, Yu Masubuchi, Fujio Toriumi
Venue: WWW
Year: 2025

We introduce the Graph-based Key Opinion Analyzer ( GKOA ), an innovative opinion mining framework that extracts key opinions from online discourse, such as product reviews or social media posts. By constructing opinion graphs using LLMs, GKOA enables the quantitative and multifaceted evaluation of opinion importance through network analysis techniques, such as centrality measures. Additionally, GKOA facilitates the detection and incorporation of overlooked opinions into refined summaries, promoting inclusivity and balance in high-stakes applications (e.g., broadcasting and policymaking). We evaluated the quality of the opinion graph in the Key Point Analysis setting, demonstrating superiority over baseline models. GKOA is available on GitHub with an introduction video.
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights
Authors: Ameet Gadekar, Aristides Gionis, Suhas Thejaswi
Venue: WWW
Year: 2025

Data summarization tasks are often modeled as k-clustering problems, where the goal is to choose k data points, called cluster centers, that best represent the dataset by minimizing a clustering objective. A popular objective is to minimize the maximum distance between any data point and its nearest center, which is formalized as the k-center problem. While in some applications all data points can be chosen as centers, in the general setting, centers must be chosen from a predefined subset of points, referred as facilities or suppliers; this is known as the k-supplier problem. In this work, we focus on fair data summarization modeled as the fair k-supplier problem, where data consists of several groups, and a minimum number of centers must be selected from each group while minimizing the k-supplier objective. The groups can be disjoint or overlapping, leading to two distinct problem variants each with different computational complexity. We present 3-approximation algorithms for both variants, improving the previously known factor of 5. For disjoint groups, our algorithm runs in polynomial time, while for overlapping groups, we present a fixed-parameter tractable algorithm, where the exponential runtime depends only on the number of groups and centers. We show that these approximation factors match the theoretical lower bounds, assuming standard complexity theory conjectures. Finally, using an open-source implementation, we demonstrate the scalability of our algorithms on large synthetic datasets and assess the price of fairness on real-world data, comparing solution quality with and without fairness constraints.
HCMRM: A High-Consistency Multimodal Relevance Model for Search Ads
Authors: Guobing Gan, Kaiming Gao, Li Wang, Shen Jiang, Peng Jiang
Venue: WWW
Year: 2025

Search advertising is essential for merchants to reach the target users on short video platforms. Short video ads aligned with user search intents are displayed through relevance matching and bid ranking mechanisms. This paper focuses on improving query-to-video relevance matching to enhance the effectiveness of ranking in ad systems. Recent vision-language pre-training models have demonstrated promise in various multimodal tasks. However, their contribution to downstream query-video relevance tasks is limited, as the alignment between the pair of visual signals and text differs from the modeling of the triplet of the query, visual signals, and video text. In addition, our previous relevance model provides limited ranking capabilities, largely due to the discrepancy between the binary cross-entropy fine-tuning objective and the ranking objective. To address these limitations, we design a high-consistency multimodal relevance model (HCMRM). It utilizes a simple yet effective method to enhance the consistency between pre-training and relevance tasks. Specifically, during the pre-training phase, along with aligning visual signals and video text, several keywords are extracted from the video text as pseudo-queries to perform the triplet relevance modeling. For the fine-tuning phase, we introduce a hierarchical softmax loss, which enables the model to learn the order within labels while maximizing the distinction between positive and negative samples. This promotes the fusion ranking of relevance and bidding in the subsequent ranking stage. The proposed method has been deployed in the Kuaishou search advertising system for over a year, contributing to a 6.1% reduction in the proportion of irrelevant ads and a 1.4% increase in ad revenue.
GraphCom: Communication Hierarchy-aware Graph Engine for Distributed Model Training
Authors: Xinbiao Gan, Tiejun Li, Liang Wu, Qiang Zhang, Lingyun Song, Bo Yang, Jie Liu, Kai Lu
Venue: WWW
Year: 2025

Efficient processing of large-scale graphs with billions to trillions of edges is essential for training graph-based large language models (LLMs) in web-scale systems. The increasing complexity and size of these models create significant communication challenges due to the extensive message exchanges required across distributed nodes. Current graph engines struggle to effectively scale across hundreds of computing nodes because they often overlook variations in communication costs within the interconnection hierarchy. This paper presents GraphCom, a communication-efficient message graph engine for graph processing on supercomputers. Our key idea is to leverage the network topology information to perform communication hierarchy-aware message aggregation, where messages are (i) gathered to the responsible nodes (referred to as monitors) in the source domains, (ii) transferred between monitors, and (iii) scattered to the target nodes in the target domains. GraphCom's aggregation is more aggressive in that each source domain (instead of the source node). We have implemented GraphCom on top of MPI. We demonstrate GraphCom's effectiveness with synthetic benchmarks and real-world graphs, utilizing up to 79,024 nodes and over 1.2 million processor cores, demonstrating that GraphCom surpasses leading graph- parallel systems and state-of-the-art counterparts in both throughput and scalability. Moreover, we have deployed GraphCom on a production supercomputer, where it consistently outperforms the top solutions on the Graph500 list. These results highlight the potential GraphCom has to significantly improve the efficiency of distributed large-scale graph-based LLM training by optimizing communication between distributed systems, making it an invaluable graph engine for distributed training tasks on web-scale graphs.
GraphCSR: A Space and Time-Efficient Sparse Matrix Representation for Web-scale Graph Processing
Authors: Xinbiao Gan, Tiejun Li, Qiang Zhang, Guang Wu, Bo Yang, Chunye Gong, Jie Liu, Kai Lu
Venue: WWW
Year: 2025

Graph data processing is essential for web-scale applications, including social networks, recommendation systems, and web of things (WoT) systems, where large, sparsely connected graphs dominate. Traditional sparse matrix storage formats like compressed sparse row (CSR) face significant memory and performance bottlenecks in distributed, federated, and edge-based computing environments, which are increasingly central to the web. To address this challenge, we propose GraphCSR, a novel storage format that clusters vertices with identical edge degrees and stores only the starting index of each group. This approach minimizes memory overhead and facilitates batch memory access while enhancing overall performance, making it particularly suitable for federated systems and resource-constrained edge nodes. Our experiments across various graph operations and large datasets show that GraphCSR achieves considerable memory savings and performance gains of large-scale, distributed graph processing. When deployed GraphCSR on two production-grade supercomputers, demonstrating its potential for scaling web and WoT graph processing in large-scale distributed computing systems.
Peng Cheng Cloud Brain and Mind Series of Large Model
Authors: Wen Gao
Venue: WWW
Year: 2025

As a revolutionary pre-training model, ChatGPT has already had a huge impact on global economic. It is the strong foundation of computing power that enables large models to continuously improve in the process of understanding massive data, resulting in breakthrough innovations. Based on the Peng Cheng Cloud Brain II E-level intelligent computing platform, Peng Cheng Laboratory is training PCL Mind Series of Large Model. Mind is the first fully autonomous, controllable, safe, open-source pre-training foundation model in China, where the performance of the 200 billion parameter base model reaches the international advanced, and the output content conforms to the Chinese core values. Peng Cheng Laboratory is opening up PCL Mind cooperation and work with external partners to continuously build a large model open-source consortium for domestic large model ecosystem. The next generation-Peng Cheng Cloud Brain III will break through key technologies such as high computing power chips, large-scale networking communication, high-performance software stacks, and large-scale parallel training, and support 10,000 chip-level parallel training of trillion-level parameter AI.
SampleLLM: Optimizing Tabular Data Synthesis in Recommendations
Authors: Jingtong Gao, Zhaocheng Du, Xiaopeng Li, Yichao Wang, Xiangyang Li, Huifeng Guo, Ruiming Tang, Xiangyu Zhao
Venue: WWW
Year: 2025

Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall short in recommender systems. This limitation arises from their difficulty in capturing complex distributions and understanding complicated feature relations from sparse and limited data, along with their inability to grasp semantic feature relations. Recently, Large Language Models (LLMs) have shown potential in generating synthetic data through few-shot learning and semantic understanding. However, they often suffer from inconsistent distribution and lack of diversity due to their inherent distribution disparity with the target dataset. To address these challenges and enhance tabular data synthesis for recommendation tasks, we propose a novel two-stage framework named SampleLLM to improve the quality of LLM-based tabular data synthesis for recommendations by ensuring better distribution alignment. In the first stage, SampleLLM employs LLMs with Chain-of-Thought prompts and diverse exemplars to generate data that closely aligns with the target dataset distribution, even when input samples are limited. The second stage uses an advanced feature attribution-based importance sampling method to refine feature relationships within the synthetic data, reducing any distribution biases introduced by the LLM. Experimental results on three recommendation datasets, two general datasets, and online deployment illustrate that SampleLLM significantly surpasses existing methods for recommendation tasks and holds promise for a broader range of tabular data scenarios.