OpenRaiser/Intern-Atlas huggingface dataset:
Beyond binary: Towards fine-grained llm-generated text detection via role recognition and involvement measurement
Authors: Zihao Cheng, Li Zhou, Feng Jiang, Benyou Wang, Haizhou Li
Venue: Conference WWW 2025
Year: 2025

The rapid development of large language models (LLMs), like ChatGPT, has resulted in the widespread presence of LLM-generated content on social media platforms, raising concerns about misinformation, data biases, and privacy violations, which can undermine trust in online discourse. While detecting LLM-generated content is crucial for mitigating these risks, current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content. This approach introduces two novel tasks: LLM Role Recognition (LLM-RR), a multi-class classification task that identifies specific roles of an LLM in content generation, and LLM Involvement Measurement (LLM-IM), a regression task that quantifies the extent of LLM involvement in content creation. To support these tasks, we propose LLMDetect, a benchmark designed to evaluate detectors' performance on these new tasks. LLMDetect includes the Hybrid News Detection Corpus (HNDC) for training detectors, as well as DetectEval, a comprehensive evaluation suite that considers five distinct cross-context variations and two multi-intensity variations within the same LLM role. This allows for a thorough assessment of detectors' generalization and robustness across diverse contexts. Our empirical validation of 10 baseline detection methods demonstrates that fine-tuned Pre-trained Language Model (PLM)-based models consistently outperform others on both tasks, while advanced LLMs face challenges in accurately detecting their own generated content. Our experimental results and analysis offer insights for developing more effective detection models for LLM-generated content. This research enhances the understanding of LLM-generated content and establishes a foundation for more nuanced detection methodologies.
The title says it all: a title term weighting strategy for ecommerce ranking
Authors: Anthony Bell, Prathyusha Senthil Kumar, Daniel Miranda
Venue: Conference CIKM 2018
Year: 2018

Search relevance is a very critical component in e-commerce applications. One of the strongest signals that determine the relevance of an item listing to an e-commerce query is the title of the item. Traditional methods for capturing this signal compare words in listing titles and the user query using tf-idf scores, or use a machine learned model with words as features and target clicks or relevance labels. Contrary to these approaches, we build a parameterized model to determine the weights of popular title terms for a query and then use these title term weights to compute the relevance of a listing title to the query. For this, we use human judged binary relevance labels of query and item title pairs as labeled data and train a model leveraging a variety of features to learn these query specific title term weights. We propose two novel approaches to model these title term weights using the relevance target and explore several novel features specific to e-commerce for this term weighting model. We use the resulting title relevance score as a feature in eBay's machine learned ranker for e-commerce search serving millions of queries each day. We observe a significant improvement over a baseline click-based binary independence model for capturing item title relevance in several metrics including model accuracy and overall relevance and engagement observed through A/B testing. We also experimentally illustrate that this feature optimized for relevance works well in conjunction with textual features optimized for demand.
Brainx: A universal brain decoding framework with feature disentanglement and neuro-geometric representation learning
Authors: Zheng Cui, Dong Nie, Pengcheng Xue, Xia Wu, Daoqiang Zhang, Xuyun Wen
Venue: Conference CIKM 2025
Year: 2025

Decoding visual stimuli from human brain activity is a fundamental challenge in cognitive neuroscience and neuroimaging. While recent advances in deep learning have significantly improved the performance of fMRI-to-image decoding, most existing methods overlook the issue of inter-subject variability in fMRI data, which leads to poor generalization across subjects. Current approaches often rely on partially shared model architectures that offer limited generalization and still require subject-specific components, restricting their applicability to unseen subjects. To address this limitation, we propose BrainX, a universal brain decoding framework that constructs a unified fMRI encoder and image generator to achieve subject-agnostic modeling. Specifically, we introduce a feature disentanglement mechanism that extracts subject-shared features from the fMRI embeddings, which are then fed into the image generator to reconstruct visual stimuli. This design eliminates the need for subject-specific models and significantly enhances cross-subject generalization. Additionally, we develop a neuro-geometric fMRI representation learning method that projects 3D cortical structures onto a 2D surface space, effectively mitigating the inaccuracies caused by imprecise geodesic distance estimation in 3D Euclidean space. Extensive experiments on the Natural Scenes Dataset (NSD) demonstrate that BrainX consistently outperforms existing state-of-the-art methods across three decoding settings: within-subject, cross-subject with finetuning, and cross-subject without finetuning.
Learning social meta-knowledge for nowcasting human mobility in disaster
Authors: Renhe Jiang, Zhaonan Wang, Yudong Tao, Chuang Yang, Xuan Song, Ryosuke Shibasaki, Shu-Ching Chen, Mei-Ling Shyu
Venue: Conference WWW 2023
Year: 2023

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level.
Non-stationary time-aware kernelized attention for temporal event prediction
Authors: Yu Ma, Zhining Liu 0001, Chenyi Zhuang, Yize Tan, Yi Dong, Wenliang Zhong, Jinjie Gu
Venue: Conference KDD 2022
Year: 2022

Modeling sequential data is essential to many applications such as natural language processing, recommendation systems, time series predictions, anomaly detection, etc. When processing sequential data, one of the critical issues is how to capture the temporal-correlation among events. Though prevalent and effective in many applications, conventional approaches such as RNNs and Transformers, struggle with handling the non-stationary characteristics (i.e., such temporal-correlation among events would change over time), which is indeed encountered in many real-world scenarios. In this paper, we present a non-stationary time-aware kernelized attention approach for input sequences of neural networks. By constructing the Generalized Spectral Mixture Kernel (GSMK), and integrating it to the attention mechanism, we mathematically reveal its representation capability in terms of the time-dependent temporal-correlation. Following that, a novel neural network structure is proposed, which would enable us to encode both stationary and non-stationary time event series. Finally, we demonstrate the performance of the proposed method on both synthetic data which presents the theoretical insights, and a variety of real-world datasets which shows its competitive performance against related work.
Mask-and contrast-enhanced spatio-temporal learning for urban flow prediction
Authors: Xu Zhang, Yongshun Gong, Xinxin Zhang, Xiaoming Wu, Chengqi Zhang, Xiangjun Dong
Venue: Conference CIKM 2023
Year: 2023

As a critical mission of intelligent transportation systems, urban flow prediction (UFP) benefits in many city services including trip planning, congestion control, and public safety. Despite the achievements of previous studies, limited efforts have been observed on simultaneous investigation of the heterogeneity in both space and time aspects. That is, regional correlations would be variable at different timestamps. In this paper, we propose a spatio-temporal learning framework with mask and contrast enhancements to capture spatio-temporal variabilities among city regions. We devise a mask-enhanced pre-training task to learn latent correlations across the spatial and temporal dimensions, and then a graph-based method is developed to extract the significance of regions by using the inter-regional attention weights. To further acquire contrastive correlations of regions, we elaborate a pre-trained contrastive learning task with the global-local cross-attention mechanism. Thereafter, two well-trained encoders have strong capability to capture latent spatio-temporal representations for the flow forecasting with time-varying. Extensive experiments conducted on real-world urban flow datasets demonstrate that our method compares favorably with other state-of-the-art models.
Promptst: Prompt-enhanced spatio-temporal multi-attribute prediction
Authors: Zijian Zhang, Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu Wang, Hongwei Zhao, Yiqi Wang, Zitao Liu
Venue: Conference CIKM 2023
Year: 2023

In the era of information explosion, spatio-temporal data mining serves as a critical part of urban management. Considering the various fields demanding attention, e.g., traffic state, human activity, and social event, predicting multiple spatio-temporal attributes simultaneously can alleviate regulatory pressure and foster smart city construction. However, current research can not handle the spatio-temporal multi-attribute prediction well due to the complex relationships between diverse attributes. The key challenge lies in how to address the common spatio-temporal patterns while tackling their distinctions. In this paper, we propose an effective solution for spatio-temporal multi-attribute prediction, PromptST. We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes. Then, we elaborate a spatio-temporal prompt tuning strategy to fit the specific attributes in a lightweight manner. Through the pretrain and prompt tuning phases, our PromptST is able to enhance the specific spatio-temoral characteristic capture by prompting the backbone model to fit the specific target attribute while maintaining the learned common knowledge. Extensive experiments on real-world datasets verify that our PromptST attains state-of-the-art performance. Furthermore, we also prove PromptST owns good transferability on unseen spatio-temporal attributes, which brings promising application potential in urban computing. The implementation code is available to ease reproducibility.
Hebbian and gradient-based plasticity enables robust memory and rapid learning in rnns
Authors: Yu Duan, Zhongfan Jia, Qian Li, Yi Zhong, Kaisheng Ma
Venue: Conference ICLR 2023
Year: 2023

Rapidly learning from ongoing experiences and remembering past events with a flexible memory system are two core capacities of biological intelligence. While the underlying neural mechanisms are not fully understood, various evidence supports that synaptic plasticity plays a critical role in memory formation and fast learning. Inspired by these results, we equip Recurrent Neural Networks (RNNs) with plasticity rules to enable them to adapt their parameters according to ongoing experiences. In addition to the traditional local Hebbian plasticity, we propose a global, gradient-based plasticity rule, which allows the model to evolve towards its self-determined target. Our models show promising results on sequential and associative memory tasks, illustrating their ability to robustly form and retain memories. In the meantime, these models can cope with many challenging few-shot learning problems. Comparing different plasticity rules under the same framework shows that Hebbian plasticity is well-suited for several memory and associative learning tasks; however, it is outperformed by gradient-based plasticity on few-shot regression tasks which require the model to infer the underlying mapping. Code is available at https://github.com/yuvenduan/PlasticRNNs.
AutoTransfer: Instance Transfer for Cross-Domain Recommendations
Authors: Jingtong Gao, Xiangyu Zhao, Bo Chen, Fan Yan, Huifeng Guo, Ruiming Tang
Venue: Conference SIGIR 2023
Year: 2023

Cross-Domain Recommendation (CDR) is a widely used approach for leveraging information from domains with rich data to assist domains with insufficient data. A key challenge of CDR research is the effective and efficient transfer of helpful information from source domain to target domain. Currently, most existing CDR methods focus on extracting implicit information from the source domain to enhance the target domain. However, the hidden structure of the extracted implicit information is highly dependent on the specific CDR model, and is therefore not easily reusable or transferable. Additionally, the extracted implicit information only appears within the intermediate substructure of specific CDRs during training and is thus not easily retained for more use. In light of these challenges, this paper proposes AutoTransfer, with an Instance Transfer Policy Network, to selectively transfers instances from source domain to target domain for improved recommendations. Specifically, AutoTransfer acts as an agent that adaptively selects a subset of informative and transferable instances from the source domain. Notably, the selected subset possesses extraordinary re-utilization property that can be saved for improving model training of various future RS models in target domain. Experimental results on two public CDR benchmark datasets demonstrate that the proposed method outperforms state-of-the-art CDR baselines and classic Single-Domain Recommendation (SDR) approaches. The implementation code is available for easy reproduction.
DTAM: Dense tracking and mapping in real-time
Authors: Richard A. Newcombe, Steven Lovegrove, Andrew J. Davison
Venue: Conference ICCV 2011
Year: 2011

DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera's 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application.
Comparing Cross-Language Query Expansion Techniques by Degrading Translation Resources
Authors: Paul McNamee, James Mayfield
Venue: Conference SIGIR 2002
Year: 2002

The quality of translation resources is arguably the most important factor affecting the performance of a cross-language information retrieval system. While many investigations have explored the use of query expansion techniques to combat errors induced by translation, no study has yet examined the effectiveness of these techniques across resources of varying quality. This paper presents results using parallel corpora and bilingual wordlists that have been deliberately degraded prior to query translation. Across different languages, translingual resources, and degrees of resource degradation, pre-translation query expansion is tremendously effective. In several instances, pre-translation expansion results in better performance when no translations are available, than when an uncompromised resource is used without pre-translation expansion. We also demonstrate that post-translation expansion using relevance feedback can confer modest performance gains. Measuring the efficacy of these techniques with resources of different quality suggests an explanation for the conflicting reports that have appeared in the literature.
Exploring folksonomy for personalized search
Authors: Shengliang Xu, Shenghua Bao, Ben Fei, Zhong Su, Yong Yu
Venue: Conference SIGIR 2008
Year: 2008

As a social service in Web 2.0, folksonomy provides the users the ability to save and organize their bookmarks online with "social annotations" or "tags". Social annotations are high quality descriptors of the web pages' topics as well as good indicators of web users' interests. We propose a personalized search framework to utilize folksonomy for personalized search. Specifically, three properties of folksonomy, namely the categorization, keyword, and structure property, are explored. In the framework, the rank of a web page is decided not only by the term matching between the query and the web page's content but also by the topic matching between the user's interests and the web page's topics. In the evaluation, we propose an automatic evaluation framework based on folksonomy data, which is able to help lighten the common high cost in personalized search evaluations. A series of experiments are conducted using two heterogeneous data sets, one crawled from Del.icio.us and the other from Dogear. Extensive experimental results show that our personalized search approach can significantly improve the search quality.
Concept Expansion Using Web Tables
Authors: Chi Wang, Kaushik Chakrabarti, Yeye He, Kris Ganjam, Zhimin Chen, Philip A. Bernstein
Venue: Conference WWW 2015
Year: 2015

We study the following problem: given the name of an ad-hoc concept as well as a few seed entities belonging to the concept, output all entities belonging to it. Since producing the exact set of entities is hard, we focus on returning a ranked list of entities. Previous approaches either use seed entities as the only input, or inherently require negative examples. They suffer from input ambiguity and semantic drift, or are not viable options for ad-hoc tail concepts. In this paper, we propose to leverage the millions of tables on the web for this problem. The core technical challenge is to identify the ``exclusive'' tables for a concept to prevent semantic drift; existing holistic ranking techniques like personalized PageRank are inadequate for this purpose. We develop novel probabilistic ranking methods that can model a new type of table-entity relationship. Experiments with real-life concepts show that our proposed solution is significantly more effective than applying state-of-the-art set expansion or holistic ranking techniques.
Challenges in building large-scale information retrieval systems: invited talk
Authors: Jeffrey Dean
Venue: Conference WSDM 2009
Year: 2009

Building and operating large-scale information retrieval systems used by hundreds of millions of people around the world provides a number of interesting challenges. Designing such systems requires making complex design tradeoffs in a number of dimensions, including (a) the number of user queries that must be handled per second and the response latency to these requests, (b) the number and size of various corpora that are searched, (c) the latency and frequency with which documents are updated or added to the corpora, and (d) the quality and cost of the ranking algorithms that are used for retrieval. In this talk I will discuss the evolution of Google's hardware infrastructure and information retrieval systems and some of the design challenges that arise from ever-increasing demands in all of these dimensions. I will also describe how we use various pieces of distributed systems infrastructure when building these retrieval systems. Finally, I will describe some future challenges and open research problems in this area.
Learning to predict response times for online query scheduling
Authors: Craig Macdonald, Nicola Tonellotto, Iadh Ounis
Venue: Conference SIGIR 2012
Year: 2012

Dynamic pruning strategies permit efficient retrieval by not fully scoring all postings of the documents matching a query -- without degrading the retrieval effectiveness of the top-ranked results. However, the amount of pruning achievable for a query can vary, resulting in queries taking different amounts of time to execute. Knowing in advance the execution time of queries would permit the exploitation of online algorithms to schedule queries across replicated servers in order to minimise the average query waiting and completion times. In this work, we investigate the impact of dynamic pruning strategies on query response times, and propose a framework for predicting the efficiency of a query. Within this framework, we analyse the accuracy of several query efficiency predictors across 10,000 queries submitted to in-memory inverted indices of a 50-million-document Web crawl. Our results show that combining multiple efficiency predictors with regression can accurately predict the response time of a query before it is executed. Moreover, using the efficiency predictors to facilitate online scheduling algorithms can result in a 22% reduction in the mean waiting time experienced by queries before execution, and a 7% reduction in the mean completion time experienced by users.
Beyond pagerank: machine learning for static ranking
Authors: Matthew Richardson, Amit Prakash, Eric Brill
Venue: Conference WWW 2006
Year: 2006

Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web. We gain a further boost in accuracy by using data on the frequency at which users visit Web pages. We use RankNet, a ranking machine learning algorithm, to combine these and other static features based on anchor text and domain characteristics. The resulting model achieves a static ranking pairwise accuracy of 67.3% (vs. 56.7% for PageRank or 50% for random).