OpenRaiser/Intern-Atlas huggingface dataset:
Towards a Better Understanding of Human Reading Comprehension with Brain Signals
Authors: Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuesong Chen, Min Zhang, Shaoping Ma
Venue: Conference WWW 2022
Year: 2022

Reading comprehension is a complex cognitive process involving many human brain activities. However, little is known about what happens in human brain during reading comprehension and how these cognitive activities can affect information retrieval process. Additionally, with the advances in brain imaging techniques such as electroencephalogram (EEG), it is possible to collect brain signals in almost real time and explore whether it can be utilized as feedback to facilitate information acquisition performance. In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension. Our findings show that neural responses vary with different types of reading contents, i.e., contents that can satisfy users’ information needs and contents that cannot. We suggest that various cognitive activities, e.g., cognitive loading, semantic-thematic understanding, and inferential processing, underpin these neural responses at the micro-time scale during reading comprehension. From these findings, we illustrate several insights for information retrieval tasks, such as ranking models construction and interface design. Besides, with the emerging of portable EEG-based applications, we suggest the possibility of detecting reading comprehension status for a proactive real-world system. To this end, we propose a Unified framework for EEG-based Reading Comprehension Modeling (UERCM). To verify its effectiveness, we conduct extensive experiments based on EEG features for two reading comprehension tasks: answer sentence classification and answer extraction. Results show that it is feasible to improve the performance of two tasks with brain signals. These findings imply that brain signals are valuable feedback for enhancing human-computer interactions during reading comprehension.
Representativeness of abortion legislation debate on twitter: A case study in argentina and chile
Authors: Eduardo Graells-Garrido, Ricardo Baeza-Yates, Mounia Lalmas
Venue: Conference WWW 2020
Year: 2020

The role of the Web in political exchange has been crucial for society. Its platforms have connected people and allowed manifestation, organization, and access to information; however, they have also produced negative outcomes, such as increased polarization and fast disinformation spreading. These types of phenomena are not completely understood in the context of continuous technological change. Here we propose to grow knowledge in these issues by focusing on representativeness, through the following question: How demographic groups are represented in the discussion on micro-blogging platforms? Our aim is to answer this question on the discussion about a specific topic, abortion, as observed on one of the most popular micro-blogging platforms. As a case study, we followed the abortion discussion on Twitter in two Spanish-speaking countries from 2015 to 2018. Our results indicate differences in representativeness with respect to country, stance, and time of publication, a process that affects to on-going legislation. These findings show that demographic groups differ in how they generate content, and that under- and over-represented groups are not the same between countries, implying that single-country outcomes are not generalizable.
Foundations of json schema
Authors: Felipe Pezoa, Juan L. Reutter, Fernando Suárez, Martín Ugarte, Domagoj Vrgoc
Venue: Conference WWW 2016
Year: 2016

JSON -- the most popular data format for sending API requests and responses -- is still lacking a standardized schema or meta-data definition that allows the developers to specify the structure of JSON documents. JSON Schema is an attempt to provide a general purpose schema language for JSON, but it is still work in progress, and the formal specification has not yet been agreed upon. Why this could be a problem becomes evident when examining the behaviour of numerous tools for validating JSON documents against this initial schema proposal: although they agree on most general cases, when presented with the greyer areas of the specification they tend to differ significantly. In this paper we provide the first formal definition of syntax and semantics for JSON Schema and use it to show that implementing this layer on top of JSON is feasible in practice. This is done both by analysing the theoretical aspects of the validation problem and by showing how to set up and validate a JSON Schema for Wikidata, the central storage for Wikimedia.
Modularized networks for few-shot hateful meme detection
Authors: Rui Cao, Roy Ka-Wei Lee, Jing Jiang
Venue: Conference WWW 2024
Year: 2024

In this paper, we address the challenge of detecting hateful memes in the low-resource setting where only a few labeled examples are available. Our approach leverages the compositionality of Low-rank adaptation (LoRA), a widely used parameter-efficient tuning technique. We commence by fine-tuning large language models (LLMs) with LoRA on selected tasks pertinent to hateful meme detection, thereby generating a suite of LoRA modules. These modules are capable of essential reasoning skills for hateful meme detection. We then use the few available annotated samples to train a module composer, which assigns weights to the LoRA modules based on their relevance. The model's learnable parameters are directly proportional to the number of LoRA modules. This modularized network, underpinned by LLMs and augmented with LoRA modules, exhibits enhanced generalization in the context of hateful meme detection. Our evaluation spans three datasets designed for hateful meme detection in a few-shot learning context. The proposed method demonstrates superior performance to traditional in-context learning, which is also more computationally intensive during inference.We then use the few available annotated samples to train a module composer, which assigns weights to the LoRA modules based on their relevance. The model's learnable parameters are directly proportional to the number of LoRA modules. This modularized network, underpinned by LLMs and augmented with LoRA modules, exhibits enhanced generalization in the context of hateful meme detection. Our evaluation spans three datasets designed for hateful meme detection in a few-shot learning context. The proposed method demonstrates superior performance to traditional in-context learning, which is also more computationally intensive during inference.
Lighter and better: Towards flexible context adaptation for retrieval augmented generation
Authors: Chenyuan Wu, Ninglu Shao, Zheng Liu, Shitao Xiao, Chaozhuo Li, Chen Zhang, Senzhang Wang, Defu Lian
Venue: Conference WSDM 2025
Year: 2025

The existing Retrieval-Augmented Generation (RAG) systems face significant challenges in terms of cost and effectiveness. On one hand, they need to encode the lengthy retrieved contexts before responding to the input tasks, which imposes substantial computational overhead. On the other hand, directly using generic Large Language Models (LLMs) often leads to sub-optimal answers, while task-specific fine-tuning may compromise the LLMs' general capabilities. To address these challenges, we introduce a novel approach called FlexRAG (Flexible Context Adaptation for RAG). In this approach, the retrieved contexts are compressed into compact embeddings before being encoded by the LLMs. Simultaneously, these compressed embeddings are optimized to enhance downstream RAG performance. A key feature of FlexRAG is its flexibility, which enables effective support for diverse compression ratios and selective preservation of important contexts. With these designs, FlexRAG achieves superior generation quality while significantly reducing running costs. The experiments across multiple QA datasets validate our approach as a cost-effective and flexible solution for RAG systems (codebase: https://github.com/wcyno23/FlexRAG).
Htmlrag: Html is better than plain text for modeling retrieved knowledge in rag systems
Authors: Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen
Venue: Conference WWW 2025
Year: 2025

Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowledge used in RAG systems, and many commercial RAG systems have used Web search engines as their major retrieval systems. Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. To address this issue, we propose HTML cleaning, compression, and a two-step block-tree-based pruning strategy, to shorten the HTML while minimizing the loss of information. Experiments on six QA datasets confirm the superiority of using HTML in RAG systems. Our code and datasets are available at https://github.com/plageon/HtmlRAG.
Dopelearning: A computational approach to rap lyrics generation
Authors: Eric Malmi, Pyry Takala, Hannu Toivonen, Tapani Raiko, Aristides Gionis
Venue: Conference KDD 2016
Year: 2016

Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.
Personalized language model learning on text data without user identifiers
Authors: Yucheng Ding, Yangwenjian Tan, Xiangyu Liu, Chaoyue Niu, Fandong Meng, Jie Zhou, Ning Liu, Fan Wu, Guihai Chen
Venue: Conference KDD 2025
Year: 2025

In many practical natural language applications, user data are highly sensitive, requiring anonymous uploads of text data from mobile devices to the cloud without user identifiers. However, the absence of user identifiers restricts the ability of cloud-based language models to provide personalized services, which are essential for catering to diverse user needs. The trivial method of replacing an explicit user identifier with a static user embedding as model input still compromises data anonymization. In this work, we propose to let each mobile device maintain a user-specific distribution to dynamically generate user embeddings, thereby breaking the one-to-one mapping between an embedding and a specific user. We further theoretically demonstrate that to prevent the cloud from tracking users via uploaded embeddings, the local distributions of different users should either be derived from a linearly dependent space to avoid identifiability or be close to each other to prevent accurate attribution. Evaluation on both public and industrial datasets using different language models reveals a remarkable improvement in accuracy from incorporating anonymous user embeddings, while preserving real-time inference requirement.
Collaboration of large language models and small recommendation models for device-cloud recommendation
Authors: Zheqi Lv, Tianyu Zhan, Wenjie Wang, Xinyu Lin, Shengyu Zhang, Wenqiao Zhang, Jiwei Li, Kun Kuang, Fei Wu
Venue: Conference KDD 2025
Year: 2025

Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to capture real-time user preferences greatly limits the practical application of LLM4Rec because (i) LLMs are costly to train and infer frequently, and (ii) LLMs struggle to access real-time data (its large number of parameters poses an obstacle to deployment on devices). Fortunately, small recommendation models (SRMs) can effectively supplement these shortcomings of LLM4Rec diagrams by consuming minimal resources for frequent training and inference, and by conveniently accessing real-time data on devices. In light of this, we designed the Device-Cloud LLM-SRM Collaborative Recommendation Framework (LSC4Rec) under a device-cloud collaboration setting. LSC4Rec aims to integrate the advantages of both LLMs and SRMs, as well as the benefits of cloud and edge computing, achieving a complementary synergy. We enhance the practicability of LSC4Rec by designing three strategies: collaborative training, collaborative inference, and intelligent request. During training, LLM generates candidate lists to enhance the ranking ability of SRM in collaborative scenarios and enables SRM to update adaptively to capture real-time user interests. During inference, LLM and SRM are deployed on the cloud and on the device, respectively. LLM generates candidate lists and initial ranking results based on user behavior, and SRM get reranking results based on the candidate list, with final results integrating both LLM's and SRM's scores. The device determines whether a new candidate list is needed by comparing the consistency of the LLM's and SRM's sorted lists. Our comprehensive and extensive experimental analysis validates the effectiveness of each strategy in LSC4Rec.
Personalized video summarization by multimodal video understanding
Authors: Brian Y. Chen, Xiangyuan Zhao, Yingnan Zhu
Venue: Conference CIKM 2024
Year: 2024

Video summarization techniques have been proven to improve the overall user experience when it comes to accessing and comprehending video content. If the user's preference is known, video summarization can identify significant information or relevant content from an input video, aiding them in obtaining the necessary information or determining their interest in watching the original video. Adapting video summarization to various types of video and user preferences requires significant training data and expensive human labeling. To facilitate such research, we proposed a new benchmark for video summarization that captures various user preferences. Also, we present a pipeline called Video Summarization with Language (VSL) for user-preferred video summarization that is based on pre-trained visual language models (VLMs) to avoid the need to train a video summarization system on a large training dataset. The pipeline takes both video and closed captioning as input and performs semantic analysis at the scene level by converting video frames into text. Subsequently, the user's genre preference was used as the basis for selecting the pertinent textual scenes. The experimental results demonstrate that our proposed pipeline outperforms current state-of-the-art unsupervised video summarization models. We show that our method is more adaptable across different datasets compared to supervised query-based video summarization models. In the end, the runtime analysis demonstrates that our pipeline is more suitable for practical use when scaling up the number of user preferences and videos.
When the music stops: Tip-of-the-tongue retrieval for music
Authors: Samarth Bhargav, Anne Schuth, Claudia Hauff
Venue: Conference SIGIR 2023
Year: 2023

We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately recall important identifying information. ToT information needs are characterized by complexity, verbosity, uncertainty, and possible false memories. We make four contributions. (1) We collect a dataset - $ToT_{Music}$ - of 2,278 information needs and ground truth answers. (2) We introduce a schema for these information needs and show that they often involve multiple modalities encompassing several Music IR subtasks such as lyric search, audio-based search, audio fingerprinting, and text search. (3) We underscore the difficulty of this task by benchmarking a standard text retrieval approach on this dataset. (4) We investigate the efficacy of query reformulations generated by a large language model (LLM), and show that they are not as effective as simply employing the entire information need as a query - leaving several open questions for future research.
Generalizable tip-of-the-tongue retrieval with llm re-ranking
Authors: Luís Borges, Rohan Jha, Jamie Callan, Bruno Martins
Venue: Conference SIGIR 2024
Year: 2024

Tip-of-the-Tongue (ToT) retrieval is challenging for search engines because the queries are usually natural-language, verbose, and contain uncertain and inaccurate information. This paper studies the generalization capabilities of existing retrieval methods with ToT queries in multiple domains. We curate a multi-domain dataset and evaluate the effectiveness of recall-oriented first-stage retrieval methods across the different domains, considering in-domain, out-of-domain, and multi-domain training settings. We further explore the use of a Large Language Model (LLM), i.e. GPT-4, for zero-shot re-ranking in various ToT domains, relying solely on the item titles. Results show that multi-domain training enhances recall, and that LLMs are strong zero-shot re-rankers, especially for popular items, outperforming direct GPT-4 prompting without first-stage retrieval. Datasets and code can be found on GitHub https://github.com/LuisPB7/TipTongue
Bdi-sen: A sentence dataset for clinical symptoms of depression
Authors: Anxo Pérez, Javier Parapar, Álvaro Barreiro, Silvia Lopez-Larrosa
Venue: Conference SIGIR 2023
Year: 2023

People tend to consider social platforms as convenient media for expressing their concerns and emotional struggles. With their widespread use, researchers could access and analyze user-generated content related to mental states. Computational models that exploit that data show promising results in detecting at-risk users based on engineered features or deep learning models. However, recent works revealed that these approaches have a limited capacity for generalization and interpretation when considering clinical settings. Grounding the models' decisions on clinical and recognized symptoms can help to overcome these limitations. In this paper, we introduce BDI-Sen, a symptom-annotated sentence dataset for depressive disorder. BDI-Sen covers all the symptoms present in the Beck Depression Inventory-II (BDI-II), a reliable questionnaire used for detecting and measuring depression. The annotations in the collection reflect whether a statement about the specific symptom is informative (i.e., exposes traces about the individual's state regarding that symptom). We thoroughly analyze this resource and explore linguistic style, emotional attribution, and other psycholinguistic markers. Additionally, we conduct a series of experiments investigating the utility of BDI-Sen for various tasks, including the detection and severity classification of symptoms. We also examine their generalization when considering symptoms from other mental diseases. BDI-Sen may aid the development of future models that consider trustworthy and valuable depression markers.
Text-based image retrieval using progressive multi-instance learning
Authors: Wen Li 0001, Lixin Duan, Dong Xu 0001, Ivor Wai-Hung Tsang
Venue: Conference ICCV 2011
Year: 2011

Relevant and irrelevant images collected from the Web (e.g., Flickr.com) have been employed as loosely labeled training data for image categorization and retrieval. In this work, we propose a new approach to learn a robust classifier for text-based image retrieval (TBIR) using relevant and irrelevant training web images, in which we explicitly handle noise in the loose labels of training images. Specifically, we first partition the relevant and irrelevant training web images into clusters. By treating each cluster as a "bag" and the images in each bag as "instances", we formulate this task as a multi-instance learning problem with constrained positive bags, in which each positive bag contains at least a portion of positive instances. We present a new algorithm called MIL-CPB to effectively exploit such constraints on positive bags and predict the labels of test instances (images). Observing that the constraints on positive bags may not always be satisfied in our application, we additionally propose a progressive scheme (referred to as Progressive MIL-CPB, or PMIL-CPB) to further improve the retrieval performance, in which we iteratively partition the top-ranked training web images from the current MIL-CPB classifier to construct more confident positive "bags "and then add these new "bags" as training data to learn the subsequent MIL-CPB classifiers. Comprehensive experiments on two challenging real-world web image data sets demonstrate the effectiveness of our approach. © 2011 IEEE.
The knowledge awakens: Keeping knowledge bases fresh with emerging entities
Authors: Johannes Hoffart, Dragan Milchevski, Gerhard Weikum, Avishek Anand, Jaspreet Singh
Venue: Conference WWW 2016
Year: 2016

Entity search over news, social media and the Web allows users to precisely retrieve concise information about specific people, organizations, movies and their characters, and other kinds of entities. This expressive search mode builds on two major assets: 1) a knowledge base (KB) that contains the entities of interest and 2) entity markup in the documents of interest derived by automatic disambiguation of entity names (NED) and linking names to the KB. These prerequisites are not easily available, though, in the important case when a user is interested in a newly emerging entity (EE) such as new movies, new songs, etc. Automatic methods for detecting and canonicalizing EEs are not nearly at the same level as the NED methods for prominent entities that have rich descriptions in the KB.
Refining Wikidata taxonomy using large language models
Authors: Yiwen Peng, Thomas Bonald, Mehwish Alam
Venue: Conference CIKM 2024
Year: 2024

Due to its collaborative nature, Wikidata is known to have a complex taxonomy, with recurrent issues like the ambiguity between instances and classes, the inaccuracy of some taxonomic paths, the presence of cycles, and the high level of redundancy across classes. Manual efforts to clean up this taxonomy are time-consuming and prone to errors or subjective decisions. We present WiKC, a new version of Wikidata taxonomy cleaned automatically using a combination of Large Language Models (LLMs) and graph mining techniques. Operations on the taxonomy, such as cutting links or merging classes, are performed with the help of zero-shot prompting on an open-source LLM. The quality of the refined taxonomy is evaluated from both intrinsic and extrinsic perspectives, on a task of entity typing for the latter, showing the practical interest of WiKC.