pwc-archive/papers-with-abstracts huggingface dataset:
SeizureTransformer: Scaling U-Net with Transformer for Simultaneous Time-Step Level Seizure Detection from Long EEG Recordings
Authors: Kerui Wu, Ziyue Zhao, Bülent Yener
Date: 2025-04-01 00:00:00
Tasks: Decoder, EEG, Seizure Detection

Epilepsy is a common neurological disorder that affects around 65 million people worldwide. Detecting seizures quickly and accurately is vital, given the prevalence and severity of the associated complications. Recently, deep learning-based automated seizure detection methods have emerged as solutions; however, most existing methods require extensive post-processing and do not effectively handle the crucial long-range patterns in EEG data. In this work, we propose SeizureTransformer, a simple model comprised of (i) a deep encoder comprising 1D convolutions (ii) a residual CNN stack and a transformer encoder to embed previous output into high-level representation with contextual information, and (iii) streamlined decoder which converts these features into a sequence of probabilities, directly indicating the presence or absence of seizures at every time step. Extensive experiments on public and private EEG seizure detection datasets demonstrate that our model significantly outperforms existing approaches (ranked in the first place in the 2025 "seizure detection challenge" organized in the International Conference on Artificial Intelligence in Epilepsy and Other Neurological Disorders), underscoring its potential for real-time, precise seizure detection.
Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks
Authors: Yuang Jia, Xiaojuan Shan, Jun Xia, Guancheng Wan, Yuchen Zhang, Wenke Huang, Mang Ye, Stan Z. Li
Date: 2025-04-01 00:00:00
Tasks: Data-free Knowledge Distillation, Knowledge Distillation

Data-free Knowledge Distillation (DFKD) is a method that constructs pseudo-samples using a generator without real data, and transfers knowledge from a teacher model to a student by enforcing the student to overcome dimensional differences and learn to mimic the teacher's outputs on these pseudo-samples. In recent years, various studies in the vision domain have made notable advancements in this area. However, the varying topological structures and non-grid nature of graph data render the methods from the vision domain ineffective. Building upon prior research into differentiable methods for graph neural networks, we propose a fast and high-quality data-free knowledge distillation approach in this paper. Without compromising distillation quality, the proposed graph-free KD method (ACGKD) significantly reduces the spatial complexity of pseudo-graphs by leveraging the Binary Concrete distribution to model the graph structure and introducing a spatial complexity tuning parameter. This approach enables efficient gradient computation for the graph structure, thereby accelerating the overall distillation process. Additionally, ACGKD eliminates the dimensional ambiguity between the student and teacher models by increasing the student's dimensions and reusing the teacher's classifier. Moreover, it equips graph knowledge distillation with a CL-based strategy to ensure the student learns graph structures progressively. Extensive experiments demonstrate that ACGKD achieves state-of-the-art performance in distilling knowledge from GNNs without training data.
Information Retrieval for Climate Impact
Authors: Maarten de Rijke, Bart van den Hurk, Flora Salim, Alaa Al Khourdajie, Nan Bai, Renato Calzone, Declan Curran, Getnet Demil, Lesley Frew, Noah Gießing, Mukesh Kumar Gupta, Maria Heuss, Sanaa Hobeichi, David Huard, Jingwei Kang, Ana Lucic, Tanwi Mallick, Shruti Nath, Andrew Okem, Barbara Pernici, Thilina Rajapakse, Hira Saleem, Harry Scells, Nicole Schneider, Damiano Spina, Yuanyuan Tian, Edmund Totin, Andrew Trotman, Ramamurthy Valavandan, Dereje Workneh, Yangxinyu Xie
Date: 2025-04-01 00:00:00
Tasks: Information Retrieval, Retrieval

The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected -- information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.
Migrating a Job Search Relevance Function
Authors: Bennett Mountain, Gabriel Womark, Ritvik Kharkar
Date: 2025-04-02 00:00:00
Tasks: Bayesian Optimization

In this paper, we describe the migration of a homebrewed C++ search engine to OpenSearch, aimed at preserving and improving search performance with minimal impact on business metrics. To facilitate the migration, we froze our job corpus and executed queries in low inventory locations to capture a representative mixture of high- and low-quality search results. These query-job pairs were labeled by crowd-sourced annotators using a custom rubric designed to reflect relevance and user satisfaction. Leveraging Bayesian optimization, we fine-tuned a new retrieval algorithm on OpenSearch, replicating key components of the original engine's logic while introducing new functionality where necessary. Through extensive online testing, we demonstrated that the new system performed on par with the original, showing improvements in specific engagement metrics, with negligible effects on revenue.
Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising
Authors: Tongtong Liu, Zhaohui Wang, Meiyue Qin, Zenghui Lu, Xudong Chen, Yuekui Yang, Peng Shu
Date: 2025-04-02 00:00:00
Tasks: Retrieval

The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query. Existing LLM-based retrieval methods generate numeric or content-based DocIDs to retrieve docs/ads. However, the one-to-few mapping between numeric IDs and docs, along with the time-consuming content extraction, leads to semantic inefficiency and limits scalability in large-scale corpora. In this paper, we propose the Real-time Ad REtrieval (RARE) framework, which leverages LLM-generated text called Commercial Intentions (CIs) as an intermediate semantic representation to directly retrieve ads for queries in real-time. These CIs are generated by a customized LLM injected with commercial knowledge, enhancing its domain relevance. Each CI corresponds to multiple ads, yielding a lightweight and scalable set of CIs. RARE has been implemented in a real-world online system, handling daily search volumes in the hundreds of millions. The online implementation has yielded significant benefits: a 5.04% increase in consumption, a 6.37% rise in Gross Merchandise Volume (GMV), a 1.28% enhancement in click-through rate (CTR) and a 5.29% increase in shallow conversions. Extensive offline experiments show RARE's superiority over ten competitive baselines in four major categories.
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval
Authors: Ming Pang, Chunyuan Yuan, Xiaoyu He, Zheng Fang, Donghao Xie, Fanyi Qu, Xue Jiang, Changping Peng, Zhangang Lin, Zheng Luo, Jingping Shao
Date: 2025-04-02 00:00:00
Tasks: General Knowledge, Retrieval, World Knowledge

Traditional sparse and dense retrieval methods struggle to leverage general world knowledge and often fail to capture the nuanced features of queries and products. With the advent of large language models (LLMs), industrial search systems have started to employ LLMs to generate identifiers for product retrieval. Commonly used identifiers include (1) static/semantic IDs and (2) product term sets. The first approach requires creating a product ID system from scratch, missing out on the world knowledge embedded within LLMs. While the second approach leverages this general knowledge, the significant difference in word distribution between queries and products means that product-based identifiers often do not align well with user search queries, leading to missed product recalls. Furthermore, when queries contain numerous attributes, these algorithms generate a large number of identifiers, making it difficult to assess their quality, which results in low overall recall efficiency. To address these challenges, this paper introduces a novel e-commerce retrieval paradigm: the Generative Retrieval and Alignment Model (GRAM). GRAM employs joint training on text information from both queries and products to generate shared text identifier codes, effectively bridging the gap between queries and products. This approach not only enhances the connection between queries and products but also improves inference efficiency. The model uses a co-alignment strategy to generate codes optimized for maximizing retrieval efficiency. Additionally, it introduces a query-product scoring mechanism to compare product values across different codes, further boosting retrieval efficiency. Extensive offline and online A/B testing demonstrates that GRAM significantly outperforms traditional models and the latest generative retrieval models, confirming its effectiveness and practicality.
GeoRAG: A Question-Answering Approach from a Geographical Perspective
Authors: Jian Wang, Zhuo Zhao, Zeng Jie Wang, Bo Da Cheng, Lei Nie, Wen Luo, Zhao Yuan Yu, Ling Wang Yuan
Date: 2025-04-02 00:00:00
Tasks: Attribute, Geographic Question Answering, Information Retrieval, Natural Language Queries, Prompt Engineering, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Geographic Question Answering (GeoQA) addresses natural language queries in geographic domains to fulfill complex user demands and improve information retrieval efficiency. Traditional QA systems, however, suffer from limited comprehension, low retrieval accuracy, weak interactivity, and inadequate handling of complex tasks, hindering precise information acquisition. This study presents GeoRAG, a knowledge-enhanced QA framework integrating domain-specific fine-tuning and prompt engineering with Retrieval-Augmented Generation (RAG) technology to enhance geographic knowledge retrieval accuracy and user interaction. The methodology involves four components: (1) A structured geographic knowledge base constructed from 3267 corpora (research papers, monographs, and technical reports), categorized via a multi-agent approach into seven dimensions: semantic understanding, spatial location, geometric morphology, attribute characteristics, feature relationships, evolutionary processes, and operational mechanisms. This yielded 145234 classified entries and 875432 multi-dimensional QA pairs. (2) A multi-label text classifier based on BERT-Base-Chinese, trained to analyze query types through geographic dimension classification. (3) A retrieval evaluator leveraging QA pair data to assess query-document relevance, optimizing retrieval precision. (4) GeoPrompt templates engineered to dynamically integrate user queries with retrieved information, enhancing response quality through dimension-specific prompting. Comparative experiments demonstrate GeoRAG's superior performance over conventional RAG across multiple base models, validating its generalizability. This work advances geographic AI by proposing a novel paradigm for deploying large language models in domain-specific contexts, with implications for improving GeoQA systems scalability and accuracy in real-world applications.
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation
Authors: Changshuo Zhang, Xiao Zhang, Teng Shi, Jun Xu, Ji-Rong Wen
Date: 2025-04-02 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Sequential recommendation is essential in modern recommender systems, aiming to predict the next item a user may interact with based on their historical behaviors. However, real-world scenarios are often dynamic and subject to shifts in user interests. Conventional sequential recommendation models are typically trained on static historical data, limiting their ability to adapt to such shifts and resulting in significant performance degradation during testing. Recently, Test-Time Training (TTT) has emerged as a promising paradigm, enabling pre-trained models to dynamically adapt to test data by leveraging unlabeled examples during testing. However, applying TTT to effectively track and address user interest shifts in recommender systems remains an open and challenging problem. Key challenges include how to capture temporal information effectively and explicitly identifying shifts in user interests during the testing phase. To address these issues, we propose T$^2$ARec, a novel model leveraging state space model for TTT by introducing two Test-Time Alignment modules tailored for sequential recommendation, effectively capturing the distribution shifts in user interest patterns over time. Specifically, T$^2$ARec aligns absolute time intervals with model-adaptive learning intervals to capture temporal dynamics and introduce an interest state alignment mechanism to effectively and explicitly identify the user interest shifts with theoretical guarantees. These two alignment modules enable efficient and incremental updates to model parameters in a self-supervised manner during testing, enhancing predictions for online recommendation. Extensive evaluations on three benchmark datasets demonstrate that T$^2$ARec achieves state-of-the-art performance and robustly mitigates the challenges posed by user interest shifts.
Hyperbolic Diffusion Recommender Model
Authors: Meng Yuan, Yutian Xiao, Wei Chen, Chu Zhao, Deqing Wang, Fuzhen Zhuang
Date: 2025-04-02 00:00:00
Tasks: model, Recommendation Systems

Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender systems, we investigate the fundamental structural disparities between images and items. Consequently, items often exhibit distinct anisotropic and directional structures that are less prevalent in images. However, the traditional forward diffusion process continuously adds isotropic Gaussian noise, causing anisotropic signals to degrade into noise, which impairs the semantically meaningful representations in recommender systems. Inspired by the advancements in hyperbolic spaces, we propose a novel \textit{\textbf{H}yperbolic} \textit{\textbf{D}iffusion} \textit{\textbf{R}ecommender} \textit{\textbf{M}odel} (named HDRM). Unlike existing directional diffusion methods based on Euclidean space, the intrinsic non-Euclidean structure of hyperbolic space makes it particularly well-adapted for handling anisotropic diffusion processes. In particular, we begin by formulating concepts to characterize latent directed diffusion processes within a geometrically grounded hyperbolic space. Subsequently, we propose a novel hyperbolic latent diffusion process specifically tailored for users and items. Drawing upon the natural geometric attributes of hyperbolic spaces, we impose structural restrictions on the space to enhance hyperbolic diffusion propagation, thereby ensuring the preservation of the intrinsic topology of user-item graphs. Extensive experiments on three benchmark datasets demonstrate the effectiveness of HDRM.
Comment Staytime Prediction with LLM-enhanced Comment Understanding
Authors: Changshuo Zhang, Zihan Lin, Shukai Liu, Yongqi Liu, Han Li
Date: 2025-04-02 00:00:00
Tasks: Prediction, Reading Comprehension

In modern online streaming platforms, the comments section plays a critical role in enhancing the overall user experience. Understanding user behavior within the comments section is essential for comprehensive user interest modeling. A key factor of user engagement is staytime, which refers to the amount of time that users browse and post comments. Existing watchtime prediction methods struggle to adapt to staytime prediction, overlooking interactions with individual comments and their interrelation. In this paper, we present a micro-video recommendation dataset with video comments (named as KuaiComt) which is collected from Kuaishou platform. correspondingly, we propose a practical framework for comment staytime prediction with LLM-enhanced Comment Understanding (LCU). Our framework leverages the strong text comprehension capabilities of large language models (LLMs) to understand textual information of comments, while also incorporating fine-grained comment ranking signals as auxiliary tasks. The framework is two-staged: first, the LLM is fine-tuned using domain-specific tasks to bridge the video and the comments; second, we incorporate the LLM outputs into the prediction model and design two comment ranking auxiliary tasks to better understand user preference. Extensive offline experiments demonstrate the effectiveness of our framework, showing significant improvements on the task of comment staytime prediction. Additionally, online A/B testing further validates the practical benefits on industrial scenario. Our dataset KuaiComt (https://github.com/lyingCS/KuaiComt.github.io) and code for LCU (https://github.com/lyingCS/LCU) are fully released.
Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools
Authors: Lena Schmidt, Oshin Sharma, Chris Marshall, Sonia Garcia Gonzalez Moral
Date: 2025-04-02 00:00:00
Tasks: Active Learning, Articles, Benchmarking, Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

Introduction: Horizon scanning in healthcare assesses early signals of innovation, crucial for timely adoption. Current horizon scanning faces challenges in efficient information retrieval and analysis, especially from unstructured sources like news, presenting a need for innovative tools. Methodology: The study introduces SCANAR and AIDOC, open-source Python-based tools designed to improve horizon scanning. SCANAR automates the retrieval and processing of news articles, offering functionalities such as de-duplication and unsupervised relevancy ranking. AIDOC aids filtration by leveraging AI to reorder textual data based on relevancy, employing neural networks for semantic similarity, and subsequently prioritizing likely relevant entries for human review. Results: Twelve internal datasets from horizon scans and four external benchmarking datasets were used. SCANAR improved retrieval efficiency by automating processes previously dependent on manual labour. AIDOC displayed work-saving potential, achieving around 62% reduction in manual review efforts at 95% recall. Comparative analysis with benchmarking data showed AIDOC's performance was similar to existing systematic review automation tools, though performance varied depending on dataset characteristics. A smaller case-study on our news datasets shows the potential of ensembling large language models within the active-learning process for faster detection of relevant articles across news datasets. Conclusion: The validation indicates that SCANAR and AIDOC show potential to enhance horizon scanning efficiency by streamlining data retrieval and prioritisation. These tools may alleviate methodological limitations and allow broader, swifter horizon scans. Further studies are suggested to optimize these models and to design new workflows and validation processes that integrate large language models.
Efficient Constant-Space Multi-Vector Retrieval
Authors: Sean MacAvaney, Antonio Mallia, Nicola Tonellotto
Date: 2025-04-02 00:00:00
Tasks: Management, Retrieval

Multi-vector retrieval methods, exemplified by the ColBERT architecture, have shown substantial promise for retrieval by providing strong trade-offs in terms of retrieval latency and effectiveness. However, they come at a high cost in terms of storage since a (potentially compressed) vector needs to be stored for every token in the input collection. To overcome this issue, we propose encoding documents to a fixed number of vectors, which are no longer necessarily tied to the input tokens. Beyond reducing the storage costs, our approach has the advantage that document representations become of a fixed size on disk, allowing for better OS paging management. Through experiments using the MSMARCO passage corpus and BEIR with the ColBERT-v2 architecture, a representative multi-vector ranking model architecture, we find that passages can be effectively encoded into a fixed number of vectors while retaining most of the original effectiveness.
Extending MovieLens-32M to Provide New Evaluation Objectives
Authors: Mark D. Smucker, Houmaan Chamani
Date: 2025-04-02 00:00:00
Tasks: Information Retrieval, Recommendation Systems

Offline evaluation of recommender systems has traditionally treated the problem as a machine learning problem. In the classic case of recommending movies, where the user has provided explicit ratings of which movies they like and don't like, each user's ratings are split into test and train sets, and the evaluation task becomes to predict the held out test data using the training data. This machine learning style of evaluation makes the objective to recommend the movies that a user has watched and rated highly, which is not the same task as helping the user find movies that they would enjoy if they watched them. This mismatch in objective between evaluation and task is a compromise to avoid the cost of asking a user to evaluate recommendations by watching each movie. We offer an extension to the MovieLens-32M dataset that provides for new evaluation objectives. Our primary objective is to predict the movies that a user would be interested in watching, i.e. predict their watchlist. To construct this extension, we recruited MovieLens users, collected their profiles, made recommendations with a diverse set of algorithms, pooled the recommendations, and had the users assess the pools. This paper demonstrates the feasibility of using pooling to construct a test collection for recommender systems. Notably, we found that the traditional machine learning style of evaluation ranks the Popular algorithm, which recommends movies based on total number of ratings in the system, in the middle of the twenty-two recommendation runs we used to build the pools. In contrast, when we rank the runs by users' interest in watching movies, we find that recommending popular movies as a recommendation algorithm becomes one of the worst performing runs. It appears that by asking users to assess their personal recommendations, we can alleviate the issue of popularity bias in the evaluation of top-n recommendation.
Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph
Authors: Lingxiao Guan, Yuanhao Huang, Jie Liu
Date: 2025-04-02 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Small Language Model

In Question Answering (QA), Retrieval Augmented Generation (RAG) has revolutionized performance in various domains. However, how to effectively capture multi-document relationships, particularly critical for biomedical tasks, remains an open question. In this work, we propose a novel method that utilizes propositional claims to construct a local knowledge graph from retrieved documents. Summaries are then derived via layerwise summarization from the knowledge graph to contextualize a small language model to perform QA. We achieved comparable or superior performance with our method over RAG baselines on several biomedical QA benchmarks. We also evaluated each individual step of our methodology over a targeted set of metrics, demonstrating its effectiveness.