OpenRaiser/Intern-Atlas huggingface dataset:
Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous Data with RAG Systems
Authors: Rishiraj Saha Roy, Joel Schlotthauer, Chris Hinze, Andreas Foltyn, Luzian Hahn, Fabian Küch
Venue: WSDM
Year: 2025

Retrieval Augmented Generation (RAG) works as a backbone for interacting with an enterprise's own data via Conversational Question Answering (ConvQA). In a RAG system, a retriever fetches passages from a collection in response to a question, which are then included in the prompt of a large language model (LLM) for generating a natural language (NL) answer. However, several RAG systems today suffer from two shortcomings: (i) retrieved passages usually contain their raw text and lack appropriate document context, negatively impacting both retrieval and answering quality; and (ii) attribution strategies that explain answer generation typically rely only on similarity between the answer and the retrieved passages, thereby only generating plausible but not causal explanations. In this work, we demonstrate RAGonite, a RAG system that remedies the above concerns by: (i) contextualizing evidence with source metadata and surrounding text; and (ii) computing counterfactual attribution, a causal explanation approach where the contribution of an evidence to an answer is determined by the similarity of the original response to the answer obtained by removing that evidence. To evaluate our proposals, we release a new benchmark ConfQuestions: it has 300 hand-created conversational questions, each in English and German, coupled with ground truth URLs, completed questions, and answers from 215 public Confluence pages. These documents are typical of enterprise wiki spaces with heterogeneous elements. Experiments with RAGonite on ConfQuestions show the viability of our ideas: contextualization improves RAG performance, and counterfactual explanations outperform standard attribution.
Building Trustworthy AI Models for Medicine: From Theory to Applications
Authors: Soumyadeep Roy, Sowmya S. Sundaram, Dominik Wolff, Niloy Ganguly
Venue: WSDM
Year: 2025

AI is emerging as an efficient companion in medicine. While AI holds promise for reducing the cognitive load of researchers and practitioners, its adoption is often hindered by a lack of trust in new AI advancements. We present sophisticated techniques for developing trustworthy artificial intelligence (AI) models in medicine, bridging breakthroughs in AI research with practical healthcare applications. We will discuss in-depth the four stages (Design, Development, Implementation, and Evaluation) involved in the process of building trustworthy AI models customized for the medical domain. We present various techniques for incorporating important Trustworthy AI principles like data privacy, robustness, explainability, interpretability, medical experts-in-the-loop, and risk assessment while developing AI models for medicine. In contrast to prior tutorials, we make the following two key contributions: (i) While explaining the 'Implementation' stage, we cover various real-world healthcare applications developed as part of research projects in academia in collaboration with medical schools in India and Germany. (ii) By including a health informatics professional as one of the tutorial organizers, we provide a fresh and much-needed perspective on the research challenges and mitigation strategies in building AI models for medicine.
Disinformation and Misinformation in the Age of Generative AI
Authors: Koustav Rudra, Niloy Ganguly, Jeanne Pia Mifsud Bonnici, Eric Müller-Budack, Ritumbra Manuvie
Venue: WSDM
Year: 2025

The rapid rise of generative AI (GenAI) technologies has revolutionized the way content is created and disseminated. As a result, highly convincing human-like malicious content including disinformation, misinformation, and propaganda can now be easily produced and distributed across the web. The diversity of generation models combined with various manipulation strategies applied to different modalities presents significant challenges for fact-checking systems and content moderation. To address this issue, we organize a workshop that focuses on harmful content that has been created intentionally (disinformation) and unintentionally (misinformation) in the era of generative AI. The workshop features specialized tracks on multimodal solutions, investigating narratives, trustworthy AI systems, and policy interventions. By bringing together experts from computer science and law, the workshop offers a comprehensive framework for combating fake content online.
Bridging Historical Subgraph Optimization and Modern Graph Neural Network Approaches in Team Recommendation
Authors: Mahdis Saeedi, Christine Wong, Hossein Fani
Venue: WSDM
Year: 2025

Team recommendation involves selecting experts with certain skills to form a successful task-oriented team. This tutorial provides a comprehensive study of conventional graph-based and a detailed review of cutting-edge neural network-based methods through unified definitions and formulations, along with insights into future research directions and real-world applications.
Improving FIM Code Completions via Context & Curriculum Based Learning
Authors: Hitesh Sagtani, Rishabh Mehrotra, Beyang Liu
Venue: WSDM
Year: 2025

Fill-in-the-Middle (FIM) models play a vital role in code completion tasks, leveraging both prefix and suffix context to provide more accurate and contextually relevant suggestions. This paper presents approaches to improve FIM code completion while addressing the challenge of maintaining low latency for real-time coding assistance. We enhance FIM code completion by incorporating context and curriculum examples in the training process. We identify patterns where completion suggestions fail more frequently, revealing complexities that smaller language models struggle with. To address these challenges, we develop a curriculum dataset by extracting hard-to-complete patterns from code repositories and generate context examples using semantic and static analysis tools (e.g. TSC compiler). We fine-tune various sized models, including StarCoder and DeepSeek, on this enhanced dataset. Our evaluation encompasses three key dimensions: the Santa Coder FIM task, the Amazon CCEval benchmark, and a new Multi-Line Infilling evaluation benchmark derived from SWE-bench. Comprehensive ablation studies across multiple model sizes reveal that while all fine-tuned models show improvements, the performance gains are more pronounced for smaller parameter models and that incorporating difficult-to-complete examples as part of curriculum learning improves completion performance. This finding is particularly sig- nificant given the latency constraints of code completion tasks. While larger models like GPT and Claude perform well in multi- line completions but are prohibitively challenging to use given high latency, and our fine-tuned models achieve a balance between per- formance and latency. Finally, we validate our approach through online A/B testing, demonstrating tangible improvements in Completion Acceptance Rate (CAR) and Completion Persistence Rate (CPR), with zero latency impact.
BioLinkerAI: Leveraging LLMs to Improve Biomedical Entity Linking and Knowledge Capture
Authors: Ahmad Sakor, Kuldeep Singh, Maria-Esther Vidal
Venue: WSDM
Year: 2025

We introduce BioLinkerAI, a neuro-symbolic framework for biomedical entity linking that integrates symbolic (domain-specific and linguistic rules) and sub-symbolic (large language models) components. Unlike traditional approaches requiring extensive labeled training data, BioLinkerAI harnesses a knowledge base and rules for candidate generation, while a pre-trained LLM handles final disambiguation. This combination ensures adaptability to diverse biomedical knowledge bases and complex entity mentions. Empirical evaluations show that BioLinkerAI surpasses state-of-the-art benchmarks, notably increasing unseen data accuracy from 65.4 to 78.5 without relying on extensive labeled datasets.
Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research Data Management
Authors: Ahmad Sakor, Mauricio Brunet, Enrique Iglesias, Ariam Rivas, Philipp D. Rohde, Angelina Kraft, Maria-Esther Vidal
Venue: WSDM
Year: 2025

Managing research digital objects (RDOs) in compliance with FAIR principles is crucial for ensuring accessibility, interoperability, and reusability across scientific domains. The Leibniz Data Manager (LDM) is a state-of-the-art framework that integrates Knowledge Graphs (KGs) and Neuro-Symbolic AI, combining the reasoning power of Large Language Models (LLMs) with structured metadata. LDM supports the management and enhancement of RDOs through entity linking, connecting datasets to external KGs like Wikidata and the Open Research Knowledge Graph (ORKG). Additionally, LDM offers federated query processing across KGs, enabling users to explore related papers, datasets, and resources through natural language questions. This demo showcases LDM's capabilities to explore RDOs, compare existing datasets, and extend metadata. By blending Neuro-Symbolic AI with FAIR and federated research data management, LDM offers a powerful tool for accelerating data-driven discovery in science. LDM is publicly accessible at https://service.tib.eu/ldmservice/.
Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval
Authors: Ferdinand Schlatt, Maik Fröbe, Matthias Hagen
Venue: WSDM
Year: 2025

A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning~IR, an easy-to-use PyTorch Lightning-based framework for applying transformer-based language models in retrieval scenarios. Lightning IR provides a modular and extensible architecture that supports all stages of a retrieval pipeline: from fine-tuning and indexing to searching and re-ranking. Designed to be scalable and reproducible, Lightning IR is available as open-source: https://github.com/webis-de/lightning-ir.
Fact-checking Multilingual Podcasts
Authors: Vinay Setty
Venue: WSDM
Year: 2025

Long-form content, such as YouTube videos and podcasts, has become widely popular, particularly among younger audiences. However, the extended format of this content presents unique challenges for fact-checking them. This talk explores the feasibility of an end-to-end solution for transcribing and fact-checking long-form content in a multilingual context. I will conclude by presenting the selected technologies and models for end-to-end fact-checking in this domain.
GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection
Authors: Lingzhi Shen, Yunfei Long, Xiaohao Cai, Imran Razzak, Guanming Chen, Kang Liu, Shoaib Jameel
Venue: WSDM
Year: 2025

Multimodal fake news detection often involves modelling heterogeneous data sources, such as vision and language. Existing detection methods typically rely on fusion effectiveness and cross-modal consistency to model the content, complicating understanding how each modality affects prediction accuracy. Additionally, these methods are primarily based on static feature modelling, making it difficult to adapt to the dynamic changes and relationships between different data modalities. This paper develops a significantly novel approach, GAMED, for multimodal modelling, which focuses on generating distinctive and discriminative features through modal decoupling to enhance cross-modal synergies, thereby optimizing overall performance in the detection process. GAMED leverages multiple parallel expert networks to refine features and pre-embed semantic knowledge to improve the experts' ability in information selection and viewpoint sharing. Subsequently, the feature distribution of each modality is adaptively adjusted based on the respective experts' opinions. GAMED also introduces a novel classification technique to dynamically manage contributions from different modalities, while improving the explainability of decisions. Experimental results on the Fakeddit and Yang datasets demonstrate that GAMED performs better than recently developed state-of-the-art models. The source code can be accessed at https://github.com/slz0925/GAMED.
Do Stubborn Users Always Cause More Polarization and Disagreement? A Mathematical Study
Authors: Mohammad Shirzadi, Ahad N. Zehmakan
Venue: WSDM
Year: 2025

We study how the stubbornness of social network users influences opinion polarization and disagreement. Our work is in the context of the popular Friedkin-Johnson opinion formation model, where users update their opinion as a function of the opinion of their connections and their own innate opinion. Stubbornness then is formulated in terms of the stress a user puts on its innate opinion. We examine two scenarios: one where all nodes have uniform stubbornness levels (homogeneous) and another where stubbornness varies among nodes (inhomogeneous). In the homogeneous scenario, we prove that as the network's stubbornness factor increases, the polarization and disagreement index grows. In the more general inhomogeneous scenario, our findings surprisingly demonstrate that increasing the stubbornness of some users (particularly, neutral/unbiased users) can reduce the polarization and disagreement. We characterize specific conditions under which this phenomenon occurs. Finally, we conduct an extensive set of experiments on real-world network data to corroborate and complement our theoretical findings.
Ventana a la Verdad (Window to the Truth): A Chatbot Application for Navigating The Colombian Truth Commission's Archives
Authors: Anna Sokol, Matthew L. Sisk, Josefina Echavarría Alvarez, Nitesh V. Chawla
Venue: WSDM
Year: 2025

We present Ventana a la Verdad, a chatbot designed to make the Clar- ification Archive and the reports of the Colombian Truth Commis- sion [6] more accessible to a wider audience. These archives contain a wealth of documents, interviews, and testimonies from Colom- bia's armed conflict, but navigating them can be challenging due to their volume and complexity. Using existing large language models (LLMs) and natural language processing techniques, our chatbot allows users to interact with the archives through natural language queries, receiving relevant and contextually appropriate responses. In the sensitive context of peace and reconciliation, where misin- formation or hallucinations can have significant adverse effects, ensuring the accuracy and reliability of information is paramount. This tool aims to facilitate better understanding and engagement with historical content, supporting educational and research efforts. We discuss the development of the chatbot, the challenges encoun- tered, and its potential impact on making the Colombian Truth Commission's archives more accessible. The chatbot is available by link here: http://ventanaverdad.lucyapps.net:1337/
The Generalization and Error Detection in LLM-based Text-to-SQL Systems
Authors: Oleg Somov
Venue: WSDM
Year: 2025

Text-to-SQL systems streamline human-database interactions, improving data retrieval and decision-making. Although large language models (LLMs) can now generate SQL code, challenges with generalization and uncontrolled generation hinder their use in production. Text-to-SQL tasks are particularly sensitive to distribution shifts, where performance declines with unfamiliar database elements or novel queries. Effective systems must maintain quality, measured in terms of generalization (correct processing of novel user requests) and error detection (identification of incorrect generations). This study empirically assesses LLM-based Text-to-SQL systems limitations, defining reliable production scenarios. Current contributions include a cross-lingual generalization research, study on generative model generalization abilities and the quality of selective classification for error detection risk under different distribution shifts in task of Text-to-SQL.
Don't Forget This: Augmenting Results with Event-Aware Search
Authors: Hugo O. Sousa, Austin R. Ward, Omar Alonso
Venue: WSDM
Year: 2025

Events like Valentine's Day and Christmas can influence user intent when interacting with search engines. For example, a user searching for gift around Valentine's Day is likely to be looking for Valentine's-themed options, whereas the same query close to Christmas would more likely suggest an interest in Holiday-themed gifts. These shifts in user intent, driven by temporal factors, are often implicit but important to determine the relevance of search results. In this demo, we explore how incorporating temporal awareness can enhance search relevance in an e-commerce setting. We constructed a database of 2K events and, using historical purchase data, developed a temporal model that estimates each event's importance on a specific date. The most relevant events on the date the query was issued are then used to enrich search results with event-specific items. Our demo illustrates how this approach enables a search system to better adapt to temporal nuances, ultimately delivering more contextually relevant products.
A Systematic Evaluation of Single-Cell Foundation Models on Cell-Type Classification Task
Authors: Nicolas Steiner, Ziteng Li, Omid Vosoughi, Johanna Schrader, Soumyadeep Roy, Wolfgang Nejdl, Ming Tang
Venue: WSDM
Year: 2025

This study presents a comprehensive benchmarking of three state-of-the-art single-cell foundation models scGPT, Geneformer, and scFoundation, on cell-type classification tasks. We evaluate the models on three datasets: myeloid, human pancreas, and multiple sclerosis, examining both standard fine-tuning and few-shot learning scenarios. Our work reveals that scFoundation consistently achieves the best performance while Geneformer performs poorly, yielding results sometimes even worse than those of the baseline models. Additionally, we demonstrate that a good foundation model can generalize well even when fine-tuned with out-of-distribution data, a capability that the baseline models lack. Our work highlights the potential of foundation models for addressing challenging biomedical questions, particularly in contexts where models are trained on one population but deployed on another.
Predicting Eviction Status Using Airbnb Data in the Absence of Ground-Truth Eviction Records
Authors: Maryam Tabar, Anusha Abdulla, J. Andrew Petersen, Dongwon Lee
Venue: WSDM
Year: 2025

The eviction of tenants is a pressing problem, which is prevalent among low-income renters in the USA, and has devastating consequences. Despite the presence of various measures to combat evictions, identifying high-need regions and tenant groups is highly challenging in many regions due to a lack of access to eviction records (partly because of some infrastructural/policy constraints). In response to this information gap, this paper proposes a solution driven by Machine Learning (ML) to monitor eviction status at various spatial resolutions using Airbnb data when ground-truth eviction data is inaccessible. In particular, we begin by demonstrating the potential of utilizing Airbnb data to build ML-driven methods for distinguishing different neighborhoods across different spatial resolutions with respect to eviction status. We then proceed to develop an ML model capable of learning eviction status levels from Airbnb data, even in the absence of ground-truth labels. Empirical evidence is presented, showcasing the model's performance on par with several robust fully-supervised ML models that had access to ground-truth labels during training. Finally, we conduct a set of cross-region tests to comprehensively study the generalizability of the achieved performance across various unseen regions in the USA that were not used during model training. The code of this project can be accessed via https://github.com/maryam-tabar/Airbnb-Eviction.
VARIUM: Variational Autoencoder for Multi-Interest Representation with Inter-User Memory
Authors: Nhu-Thuat Tran, Hady W. Lauw
Venue: WSDM
Year: 2025

Frameworks for discovering multiple user interest factors based on Variational AutoEncoder (VAE) has demonstrated competitive recommendation performance. However, as VAE only considers one user as input at a time, sharing across like-minded users may not be adequately facilitated. Moreover, interest sharing between users is not always available and thus, poses a challenge for VAE to explicitly model this information. To resolve this, we introduce an inter-user memory-based mechanism to unsupervisedly discover latent interest sharing between users under VAE framework. Concretely, we design a memory including an array of prototypes, each hypothetically representing a group of users sharing a particular interest. These memory prototypes are jointly trained with the backbone VAE-based recommendation model. For each user, we first discover multiple intra-user interest factors behind their item adoptions. Next, intra-user interest factors query to memory to retrieve the inter-user interest clues from like-minded users. This query-retrieve process is performed sequentially via a series of attention-transformation steps. Then, interest clues retrieved from memory are incorporated into interest factor representations of each user to increase their expressiveness. Thorough experiments on real-world datasets verify the strength of our method over an array of baselines. We further conduct qualitative analysis to understand the inner working of our memory-based refinement approach.
LiveFC: A System for Live Fact-Checking of Audio Streams
Authors: Venktesh V, Vinay Setty
Venue: WSDM
Year: 2025

The advances in the digital era have led to rapid dissemination of information. This has also aggravated the spread of misinformation and disinformation. This has potentially serious consequences, such as civil unrest. While fact-checking aims to combat this, manual fact-checking is cumbersome and not scalable. While automated fact-checking approaches exist, they do not operate in real-time and do not always account for spread of misinformation through different modalities. This is particularly important as proactive fact-checking on live streams in real-time can help people be informed of false narratives and prevent catastrophic consequences that may cause civil unrest. This is particularly relevant with the rapid dissemination of information through video on social media platforms or other streams like political rallies and debates. Hence, in this work we develop a platform named LiveFC, that can aid in fact-checking live audio streams in real-time. LiveFC has a user-friendly interface that displays the claims detected along with their veracity and evidence for live streams with associated speakers for claims from respective segments. The app can be accessed at http://livefc.factiverse.ai and a screen recording of the demo can be found at https://bit.ly/3WVAoIw.