OpenRaiser/Intern-Atlas huggingface dataset:
Extracting cultural commonsense knowledge at scale
Authors: Tuan-Phong Nguyen, Simon Razniewski, Aparna S. Varde, Gerhard Weikum
Venue: Conference WWW 2023
Year: 2023

Structured knowledge is important for many AI applications. Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledge projects. However, they lack knowledge about human traits and behaviors conditioned on socio-cultural contexts, which is crucial for situative AI. This paper presents Candle, an end-to-end methodology for extracting high-quality cultural commonsense knowledge (CCSK) at scale. Candle extracts CCSK assertions from a huge web corpus and organizes them into coherent clusters, for 3 domains of subjects (geography, religion, occupation) and several cultural facets (food, drinks, clothing, traditions, rituals, behaviors). Candle includes judicious techniques for classification-based filtering and scoring of interestingness. Experimental evaluations show the superiority of the Candle CCSK collection over prior works, and an extrinsic use case demonstrates the benefits of CCSK for the GPT-3 language model. Code and data can be accessed at https://candle.mpi-inf.mpg.de/.
A simple but effective bidirectional framework for relational triple extraction
Authors: Feiliang Ren, Longhui Zhang, Xiaofeng Zhao, Shujuan Yin, Shilei Liu, Bochao Li
Venue: Conference WSDM 2022
Year: 2022

Tagging based relational triple extraction methods are attracting growing research attention recently. However, most of these methods take a unidirectional extraction framework that first extracts all subjects and then extracts objects and relations simultaneously based on the subjects extracted. This framework has an obvious deficiency that it is too sensitive to the extraction results of subjects. To overcome this deficiency, we propose a bidirectional extraction framework based method that extracts triples based on the entity pairs extracted from two complementary directions. Concretely, we first extract all possible subject-object pairs from two paralleled directions. These two extraction directions are connected by a shared encoder component, thus the extraction features from one direction can flow to another direction and vice versa. By this way, the extractions of two directions can boost and complement each other. Next, we assign all possible relations for each entity pair by a biaffine model. During training, we observe that the share structure will lead to a convergence rate inconsistency issue which is harmful to performance. So we propose a share-aware learning mechanism to address it. We evaluate the proposed model on multiple benchmark datasets. Extensive experimental results show that the proposed model is very effective and it achieves state-of-the-art results on all of these datasets. Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods. The source code of our work is available at: https://github.com/neukg/BiRTE.
Flashrag: A modular toolkit for efficient retrieval-augmented generation research
Authors: Jiajie Jin, Yutao Zhu, Zhicheng Dou, Guanting Dong, Xinyu Yang, Chenghao Zhang, Tong Zhao, Zhao Yang, Ji-Rong Wen
Venue: Conference WWW 2025
Year: 2025

With the advent of large language models (LLMs) and multimodal large language models (MLLMs), the potential of retrieval-augmented generation (RAG) has attracted considerable research attention. Various novel algorithms and models have been introduced to enhance different aspects of RAG systems. However, the absence of a standardized framework for implementation, coupled with the inherently complex RAG process, makes it challenging and time-consuming for researchers to compare and evaluate these approaches in a consistent environment. Existing RAG toolkits, such as LangChain and LlamaIndex, while available, are often heavy and inflexibly, failing to meet the customization needs of researchers. In response to this challenge, we develop \ours{}, an efficient and modular open-source toolkit designed to assist researchers in reproducing and comparing existing RAG methods and developing their own algorithms within a unified framework. Our toolkit has implemented 16 advanced RAG methods and gathered and organized 38 benchmark datasets. It has various features, including a customizable modular framework, multimodal RAG capabilities, a rich collection of pre-implemented RAG works, comprehensive datasets, efficient auxiliary pre-processing scripts, and extensive and standard evaluation metrics. Our toolkit and resources are available at https://github.com/RUC-NLPIR/FlashRAG.
Scale-up: An efficient black-box input-level backdoor detection via analyzing scaled prediction consistency
Authors: Junfeng Guo, Yiming Li 0004, Xun Chen, Hanqing Guo, Lichao Sun 0001, Cong Liu 0005
Venue: Conference ICLR 2023
Year: 2023

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries embed a hidden backdoor trigger during the training process for malicious prediction manipulation. These attacks pose great threats to the applications of DNNs under the real-world machine learning as a service (MLaaS) setting, where the deployed model is fully black-box while the users can only query and obtain its predictions. Currently, there are many existing defenses to reduce backdoor threats. However, almost all of them cannot be adopted in MLaaS scenarios since they require getting access to or even modifying the suspicious models. In this paper, we propose a simple yet effective black-box input-level backdoor detection, called SCALE-UP, which requires only the predicted labels to alleviate this problem. Specifically, we identify and filter malicious testing samples by analyzing their prediction consistency during the pixel-wise amplification process. Our defense is motivated by an intriguing observation (dubbed scaled prediction consistency) that the predictions of poisoned samples are significantly more consistent compared to those of benign ones when amplifying all pixel values. Besides, we also provide theoretical foundations to explain this phenomenon. Extensive experiments are conducted on benchmark datasets, verifying the effectiveness and efficiency of our defense and its resistance to potential adaptive attacks. Our codes are available at https://github.com/JunfengGo/SCALE-UP.
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Authors: Jiacheng Li 0003, Ming Wang, Jin Li 0003, Jinmiao Fu, Xin Shen, Jingbo Shang, Julian J. McAuley
Venue: Conference KDD 2023
Year: 2023

Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge to new datasets. In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets. To this end, we present a novel framework, named Recformer, which effectively learns language representations for sequential recommendation. Specifically, we propose to formulate an item as a "sentence" (word sequence) by flattening item key-value attributes described by text so that an item sequence for a user becomes a sequence of sentences. For recommendation, Recformer is trained to understand the "sentence" sequence and retrieve the next "sentence". To encode item sequences, we design a bi-directional Transformer similar to the model Longformer but with different embedding layers for sequential recommendation. For effective representation learning, we propose novel pretraining and finetuning methods which combine language understanding and recommendation tasks. Therefore, Recformer can effectively recommend the next item based on language representations. Extensive experiments conducted on six datasets demonstrate the effectiveness of Recformer for sequential recommendation, especially in low-resource and cold-start settings.
Prompt distillation for efficient llm-based recommendation
Authors: Lei Li, Yongfeng Zhang, Li Chen
Venue: Conference CIKM 2023
Year: 2023

Large language models (LLM) have manifested unparalleled modeling capability on various tasks, e.g., multi-step reasoning, but the input to these models is mostly limited to plain text, which could be very long and contain noisy information. Long text could take long time to process, and thus may not be efficient enough for recommender systems that require immediate response. In LLM-based recommendation models, user and item IDs are usually filled in a template (i.e., discrete prompt) to allow the models to understand a given task, but the models usually need extensive fine-tuning to bridge the user/item IDs and the template words and to unleash the power of LLM for recommendation. To address the problems, we propose to distill the discrete prompt for a specific task to a set of continuous prompt vectors so as to bridge IDs and words and to reduce the inference time. We also design a training strategy with an attempt to improve the efficiency of training these models. Experimental results on three real-world datasets demonstrate the effectiveness of our PrOmpt Distillation (POD) approach on both sequential recommendation and top-N recommendation tasks. Although the training efficiency can be significantly improved, the improvement of inference efficiency is limited. This finding may inspire researchers in the community to further improve the inference efficiency of LLM-based recommendation models.
Bridging items and language: A transition paradigm for large language model-based recommendation
Authors: Xinyu Lin 0001, Wenjie Wang 0007, Yongqi Li 0001, Fuli Feng, See-Kiong Ng, Tat-Seng Chua
Venue: Conference KDD 2024
Year: 2024

Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space: 1) item indexing utilizes identifiers to represent items in the language space, and 2) generation grounding associates LLMs' generated token sequences to in-corpus items. However, previous methods exhibit inherent limitations in the two steps. Existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) either lose semantics or lack adequate distinctiveness. Moreover, prior generation grounding methods might generate invalid identifiers, thus misaligning with in-corpus items. To address these issues, we propose a novel Transition paradigm for LLM-based Recommender (named TransRec) to bridge items and language. Specifically, TransRec presents multi-facet identifiers, which simultaneously incorporate ID, title, and attribute for item indexing to pursue both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to ensure generating valid identifiers only and utilize substring indexing to encourage LLMs to generate from any position of identifiers. Lastly, TransRec presents an aggregated grounding module to leverage generated multi-facet identifiers to rank in-corpus items efficiently. We instantiate TransRec on two backbone models, BART-large and LLaMA-7B. Extensive results on three real-world datasets under diverse settings validate the superiority of TransRec.
Better to ask in english: Cross-lingual evaluation of large language models for healthcare queries
Authors: Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu, Munmun De Choudhury, Srijan Kumar
Venue: Conference WWW 2024
Year: 2024

Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like healthcare, where lay individuals are increasingly appropriating LLMs as conversational agents for everyday queries. While LLMs demonstrate impressive language understanding and generation proficiencies, concerns regarding their safety remain paramount in these high-stake domains. Moreover, the development of LLMs is disproportionately focused on English. It remains unclear how these LLMs perform in the context of non-English languages, a gap that is critical for ensuring equity in the real-world use of these systems. This paper provides a framework to investigate the effectiveness of LLMs as multi-lingual dialogue systems for healthcare queries. Our empirically-derived framework XlingEval focuses on three fundamental criteria for evaluating LLM responses to naturalistic human-authored health-related questions: correctness, consistency, and verifiability. Through extensive experiments on four major global languages, including English, Spanish, Chinese, and Hindi, spanning three expert-annotated large health Q&A datasets, and through an amalgamation of algorithmic and human-evaluation strategies, we found a pronounced disparity in LLM responses across these languages, indicating a need for enhanced cross-lingual capabilities. We further propose XLingHealth, a cross-lingual benchmark for examining the multilingual capabilities of LLMs in the healthcare context. Our findings underscore the pressing need to bolster the cross-lingual capacities of these models, and to provide an equitable information ecosystem accessible to all.
Trawling the Web for Emerging Cyber-communities
Authors: Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins
Venue: Conference WWW 1999
Year: 1999

The Web harbors a large number of communities — groups of content-creators sharing a common interest — each of which manifests itself as a set of interlinked Web pages. Newgroups and commercial Web directories together contain of the order of 20,000 such communities; our particular interest here is on emerging communities — those that have little or no representation in such fora. The subject of this paper is the systematic enumeration of over 100,000 such emerging communities from a Web crawl: we call our process trawling. We motivate a graph-theoretic approach to locating such communities, and describe the algorithms, and the algorithmic engineering necessary to find structures that subscribe to this notion, the challenges in handling such a huge data set, and the results of our experiment. © 1999 Published by Elsevier Science B.V. All rights reserved.
Name-It: Association of face and name in video
Authors: Shin&apos, ichi Satoh 0001, Takeo Kanade
Venue: Conference CVPR 1997
Year: 1997

This paper proposes a novel approach to extract meaningful content information from video by collaborative integration of image understanding and natural language processing. As an actual example, we developed a system that associates faces and names in videos, called Name-It, which is given news videos as a knowledge source, then automatically extracts face and name association as content information. The system can infer the name of a given unknown face image, or guess faces which are likely to have the name given to the system. This paper explains the method with several successful matching results which reveal effectiveness in integrating heterogeneous techniques as well as the importance of real content information extraction from video, especially face-name association.
Efficient similarity joins for near duplicate detection
Authors: Chuan Xiao, Wei Wang, Xuemin Lin, Jeffrey Xu Yu
Venue: Conference WWW 2008
Year: 2008

With the increasing amount of data and the need to integrate data from multiple data sources, a challenging issue is to find near duplicate records efficiently. In this paper, we focus on efficient algorithms to find pairs of records such that their similarities are above a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. Experimental results show that our proposed algorithms can achieve up to 2.6x - 5x speed-up over previous algorithms on several real datasets and provide alternative solutions to the near duplicate Web page detection problem.
Improving post-click user engagement on native ads via survival analysis
Authors: Nicola Barbieri, Fabrizio Silvestri, Mounia Lalmas
Venue: Conference WWW 2016
Year: 2016

In this paper we focus on estimating the post-click engagement on native ads by predicting the dwell time on the corresponding ad landing pages. To infer relationships between features of the ads and dwell time we resort to the application of survival analysis techniques, which allow us to estimate the distribution of the length of time that the user will spend on the ad. This information is then integrated into the ad ranking function with the goal of promoting the rank of ads that are likely to be clicked and consumed by users (dwell time greater than a given threshold). The online evaluation over live traffic shows that considering post-click engagement has a consistent positive effect on both CTR, decreases the number of bounces and increases the average dwell time, hence leading to a better user post-click experience.
Modeling dwell time to predict click-level satisfaction
Authors: Youngho Kim, Ahmed Hassan Awadallah, Ryen W. White, Imed Zitouni
Venue: Conference WSDM 2014
Year: 2014

Clicks on search results are the most widely used behavioral signals for predicting search satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy. Previous work has shown that clicks are affected by position bias, caption bias, and other factors. A popular heuristic for reducing this noise is to only consider clicks with long dwell time, usually equaling or exceeding 30 seconds. The rationale is that the more time a searcher spends on a page, the more likely they are to be satisfied with its contents. However, having a single threshold value assumes that users need a fixed amount of time to be satisfied with any result click, irrespective of the page chosen. In reality, clicked pages can differ significantly. Pages have different topics, readability levels, content lengths, etc. All of these factors may affect the amount of time spent by the user on the page. In this paper, we study the effect of different page characteristics on the time needed to achieve search satisfaction. We show that the topic of the page, its length and its readability level are critical in determining the amount of dwell time needed to predict whether any click is associated with satisfaction. We propose a method to model and provide a better understanding of click dwell time. We estimate click dwell time distributions for SAT (satisfied) or DSAT (dissatisfied) clicks for different click segments and use them to derive features to train a click-level satisfaction model. We compare the proposed model to baseline methods that use dwell time and other search performance predictors as features, and demonstrate that the proposed model achieves significant improvements.
Understanding User Attention and Engagement in Online News Reading
Authors: Dmitry Lagun, Mounia Lalmas
Venue: Conference WSDM 2016
Year: 2016

Prior work on user engagement with online media identified web page dwell time as a key metric reflecting level of user engagement with online news articles. While on average, dwell time gives a reasonable estimate of user experience with a news article, it is not able to capture important aspects of user interaction with the page, such as how much time a user spends reading the article vs. viewing the comment posted by other users, or the actual proportion of article read by the user. In this paper, we propose a set of user engagement classes along with new user engagement metrics that, unlike dwell time, more accurately reflect user experience with the content. Our user engagement classes provide clear and interpretable taxonomy of user engagement with online news, and are defined based on amount of time user spends on the page, proportion of the article user actually reads and the amount of interaction users performs with the comments. Moreover, we demonstrate that our metrics are relatively easier to predict from the news article content, compared to the dwell time, making optimization of user engagement more attainable goal.
Promoting Positive Post-Click Experience for In-Stream Yahoo Gemini Users
Authors: Mounia Lalmas, Janette Lehmann, Guy Shaked, Fabrizio Silvestri, Gabriele Tolomei
Venue: Conference KDD 2015
Year: 2015

Click-through rate (CTR) is the most common metric used to assess the performance of an online advert; another performance of an online advert is the user post-click experience. In this paper, we describe the method we have implemented in Yahoo Gemini to measure the post-click experience on Yahoo mobile news streams via an automatic analysis of advert landing pages. We measure the post-click experience by means of two well-known metrics, dwell time and bounce rate. We show that these metrics can be used as proxy of an advert post-click experience, and that a negative post-click experience has a negative effect on user engagement and future ad clicks. We then put forward an approach that analyses advert landing pages, and show how these can affect dwell time and bounce rate. Finally, we develop a prediction model for advert quality based on dwell time, which was deployed on Yahoo mobile news stream app running on iOS. The results show that, using dwell time as a proxy of post-click experience, we can prioritise higher quality ads. We demonstrate the impact of this on users via A/B testing.
Understanding web browsing behaviors through Weibull analysis of dwell time
Authors: Chao Liu, Ryen W. White, Susan T. Dumais
Venue: Conference SIGIR 2010
Year: 2010

Dwell time on Web pages has been extensively used for various information retrieval tasks. However, some basic yet important questions have not been sufficiently addressed, eg, what distribution is appropriate to model the distribution of dwell times on a Web page, and furthermore, what the distribution tells us about the underlying browsing behaviors. In this paper, we draw an analogy between abandoning a page during Web browsing and a system failure in reliability analysis, and propose to model the dwell time using the Weibull distribution. Using this distribution provides better goodness-of-fit to real world data, and it uncovers some interesting patterns of user browsing behaviors not previously reported. For example, our analysis reveals that Web browsing in general exhibits a significant "negative aging" phenomenon, which means that some initial screening has to be passed before a page is examined in detail, giving rise to the browsing behavior that we call "screen-and-glean." In addition, we demonstrate that dwell time distributions can be reasonably predicted purely based on low-level page features, which broadens the possible applications of this study to situations where log data may be unavailable.