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A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE arXiv.cs.IR Pub Date : 2024-03-15 Hervé Déjean, Stéphane Clinchant, Thibault Formal
We present a comparative study between cross-encoder and LLMs rerankers in the context of re-ranking effective SPLADE retrievers. We conduct a large evaluation on TREC Deep Learning datasets and out-of-domain datasets such as BEIR and LoTTE. In the first set of experiments, we show how cross-encoder rerankers are hard to distinguish when it comes to re-rerank SPLADE on MS MARCO. Observations shift
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The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation arXiv.cs.IR Pub Date : 2024-03-15 Lei Wang, Ee-Peng Lim
Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, and number of demonstrations. As increasing the number of demonstrations in ICL does not improve accuracy
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PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction arXiv.cs.IR Pub Date : 2024-03-15 Yuanbo Gao, Peng Lin, Dongyue Wang, Feng Mei, Xiwei Zhao, Sulong Xu, Jinghe Hu
Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that have prevailed for decades. On one hand, IDRec often faces significant performance degradation on cold-start problem; on the other hand, IDRec cannot use longer training data due to constraints imposed by iteration efficiency
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VideoAgent: Long-form Video Understanding with Large Language Model as Agent arXiv.cs.IR Pub Date : 2024-03-15 Xiaohan Wang, Yuhui Zhang, Orr Zohar, Serena Yeung-Levy
Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the human cognitive process for long-form video understanding, we emphasize interactive reasoning and planning over the ability to process lengthy visual inputs. We introduce a novel agent-based system, VideoAgent, that employs
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DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models arXiv.cs.IR Pub Date : 2024-03-15 Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, Yiqun Liu
Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). There are two key elements of this paradigm: identifying the optimal moment to activate the retrieval module (deciding when to retrieve) and crafting the appropriate query once retrieval is triggered (determining what to retrieve). However
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Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction arXiv.cs.IR Pub Date : 2024-03-15 Ziyang Xu, Keqin Peng, Liang Ding, Dacheng Tao, Xiliang Lu
Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction, i.e., prompts tend to introduce biases toward specific labels. However, the extent and impact of prompt bias within the model remain underexplored. In response, this paper quantifies the bias with various types of prompts and assesses their impact on different benchmarks. We show
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Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation arXiv.cs.IR Pub Date : 2024-03-13 Se-eun Yoon, Zhankui He, Jessica Maria Echterhoff, Julian McAuley
Synthetic users are cost-effective proxies for real users in the evaluation of conversational recommender systems. Large language models show promise in simulating human-like behavior, raising the question of their ability to represent a diverse population of users. We introduce a new protocol to measure the degree to which language models can accurately emulate human behavior in conversational recommendation
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Logical Discrete Graphical Models Must Supplement Large Language Models for Information Synthesis arXiv.cs.IR Pub Date : 2024-03-14 Gregory Coppola
Given the emergent reasoning abilities of large language models, information retrieval is becoming more complex. Rather than just retrieve a document, modern information retrieval systems advertise that they can synthesize an answer based on potentially many different documents, conflicting data sources, and using reasoning. We review recent literature and argue that the large language model has crucial
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Seed-based information retrieval in networks of research publications: Evaluation of direct citations, bibliographic coupling, co-citations and PubMed related article score arXiv.cs.IR Pub Date : 2024-03-14 Peter Sjögårde, Per Ahlgren
In this contribution, we deal with seed-based information retrieval in networks of research publications. Using systematic reviews as a baseline, and publication data from the NIH Open Citation Collection, we compare the performance of the three citation-based approaches direct citation, co-citation, and bibliographic coupling with respect to recall and precision measures. In addition, we include the
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Online and Offline Evaluation in Search Clarification arXiv.cs.IR Pub Date : 2024-03-14 Leila Tavakoli, Johanne R. Trippas, Hamed Zamani, Falk Scholer, Mark Sanderson
The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification questions evaluated through human assessment
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USimAgent: Large Language Models for Simulating Search Users arXiv.cs.IR Pub Date : 2024-03-14 Erhan Zhang, Xingzhu Wang, Peiyuan Gong, Yankai Lin, Jiaxin Mao
Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. Nonetheless, accurately simulating user search behaviors has long been a challenge, because users' actions in search are highly complex and driven by intricate cognitive processes such as learning, reasoning, and planning
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Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization arXiv.cs.IR Pub Date : 2024-03-14 Jinsheng Li, Wei Cui, Xu Zhang
Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous non-convex gradient methods only utilize asymmetric factorization to achieve spectral compressed sensing. In this paper, we propose a novel nonconvex projected gradient descent method for spectral compressed sensing via
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Domain Adaptation for Dense Retrieval and Conversational Dense Retrieval through Self-Supervision by Meticulous Pseudo-Relevance Labeling arXiv.cs.IR Pub Date : 2024-03-13 Minghan Li, Eric Gaussier
Recent studies have demonstrated that the ability of dense retrieval models to generalize to target domains with different distributions is limited, which contrasts with the results obtained with interaction-based models. Prior attempts to mitigate this challenge involved leveraging adversarial learning and query generation approaches, but both approaches nevertheless resulted in limited improvements
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Evaluating LLMs for Gender Disparities in Notable Persons arXiv.cs.IR Pub Date : 2024-03-14 Lauren Rhue, Sofie Goethals, Arun Sundararajan
This study examines the use of Large Language Models (LLMs) for retrieving factual information, addressing concerns over their propensity to produce factually incorrect "hallucinated" responses or to altogether decline to even answer prompt at all. Specifically, it investigates the presence of gender-based biases in LLMs' responses to factual inquiries. This paper takes a multi-pronged approach to
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AcademiaOS: Automating Grounded Theory Development in Qualitative Research with Large Language Models arXiv.cs.IR Pub Date : 2024-03-13 Thomas Übellacker
AcademiaOS is a first attempt to automate grounded theory development in qualitative research with large language models. Using recent large language models' language understanding, generation, and reasoning capabilities, AcademiaOS codes curated qualitative raw data such as interview transcripts and develops themes and dimensions to further develop a grounded theoretical model, affording novel insights
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ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation arXiv.cs.IR Pub Date : 2024-03-13 Sayar Ghosh Roy, Jiawei Han
Existing Machine Learning approaches for local citation recommendation directly map or translate a query, which is typically a claim or an entity mention, to citation-worthy research papers. Within such a formulation, it is challenging to pinpoint why one should cite a specific research paper for a particular query, leading to limited recommendation interpretability. To alleviate this, we introduce
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NLQxform-UI: A Natural Language Interface for Querying DBLP Interactively arXiv.cs.IR Pub Date : 2024-03-13 Ruijie Wang, Zhiruo Zhang, Luca Rossetto, Florian Ruosch, Abraham Bernstein
In recent years, the DBLP computer science bibliography has been prominently used for searching scholarly information, such as publications, scholars, and venues. However, its current search service lacks the capability to handle complex queries, which limits the usability of DBLP. In this paper, we present NLQxform-UI, a web-based natural language interface that enables users to query DBLP directly
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Discrete Semantic Tokenization for Deep CTR Prediction arXiv.cs.IR Pub Date : 2024-03-13 Qijiong Liu, Hengchang Hu, Jiahao Wu, Jieming Zhu, Min-Yen Kan, Xiao-Ming Wu
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios. The content-encoding paradigm, which integrates user and item encoders directly into CTR models, prioritizes space over time. In contrast, the embedding-based paradigm transforms item and user semantics into latent embeddings
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DESERE: The 1st Workshop on Decentralised Search and Recommendation arXiv.cs.IR Pub Date : 2024-03-12 Mohamed Ragab, Yury Savateev, Wenjie Wang, Reza Moosaei, Thanassis Tiropanis, Alexandra Poulovassilis, Adriane Chapman, Helen Oliver, George Roussos
The DESERE Workshop, our First Workshop on Decentralised Search and Recommendation, offers a platform for researchers to explore and share innovative ideas on decentralised web services, mainly focusing on three major topics: (i) societal impact of decentralised systems: their effect on privacy, policy, and regulation; (ii) decentralising applications: algorithmic and performance challenges that arise
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Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models arXiv.cs.IR Pub Date : 2024-03-12 Andrew Parry, Maik Fröbe, Sean MacAvaney, Martin Potthast, Matthias Hagen
Modern sequence-to-sequence relevance models like monoT5 can effectively capture complex textual interactions between queries and documents through cross-encoding. However, the use of natural language tokens in prompts, such as Query, Document, and Relevant for monoT5, opens an attack vector for malicious documents to manipulate their relevance score through prompt injection, e.g., by adding target
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Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness arXiv.cs.IR Pub Date : 2024-03-12 Mingyue Cheng, Hao Zhang, Qi Liu, Fajie Yuan, Zhi Li, Zhenya Huang, Enhong Chen, Jun Zhou, Longfei Li
Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{collaborative signals} from behaviors does not fully grasp user interests. It is also significant to model the \textit{semantic relatedness} reflected in content features, e.g., images and text. Towards that end, in this paper
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Proactive Recommendation with Iterative Preference Guidance arXiv.cs.IR Pub Date : 2024-03-12 Shuxian Bi, Wenjie Wang, Hang Pan, Fuli Feng, Xiangnan He
Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization. To counteract this, proactive recommendation actively steers users towards developing new
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The future of document indexing: GPT and Donut revolutionize table of content processing arXiv.cs.IR Pub Date : 2024-03-12 Degaga Wolde Feyisa, Haylemicheal Berihun, Amanuel Zewdu, Mahsa Najimoghadam, Marzieh Zare
Industrial projects rely heavily on lengthy, complex specification documents, making tedious manual extraction of structured information a major bottleneck. This paper introduces an innovative approach to automate this process, leveraging the capabilities of two cutting-edge AI models: Donut, a model that extracts information directly from scanned documents without OCR, and OpenAI GPT-3.5 Turbo, a
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LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries arXiv.cs.IR Pub Date : 2024-03-12 Ziqi Yin, Shanshan Feng, Shang Liu, Gao Cong, Yew Soon Ong, Bin Cui
With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that evaluates both spatial and textual relevance, have found many real-life applications. Existing geo-textual indexes for TkQs use traditional retrieval models like BM25 to compute text relevance and usually exploit a simple linear function to compute
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Self-supervised Contrastive Learning for Implicit Collaborative Filtering arXiv.cs.IR Pub Date : 2024-03-12 Shipeng Song, Bin Liu, Fei Teng, Tianrui Li
Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation, particularly with BPR as a representative ranking prediction task that dominates implicit collaborative filtering. However, the presence of false-positive and false-negative examples in recommendation systems hampers accurate preference learning. In this study, we propose a
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Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams arXiv.cs.IR Pub Date : 2024-03-11 Andy Skumanich, Han Kyul Kim
Social media platforms hold valuable insights, yet extracting essential information can be challenging. Traditional top-down approaches often struggle to capture critical signals in rapidly changing events. As global events evolve swiftly, social media narratives, including instances of disinformation, become significant sources of insights. To address the need for an inductive strategy, we explore
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MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation arXiv.cs.IR Pub Date : 2024-03-11 Wenhao Wu, Jialiang Zhou, Ailong He, Shuguang Han, Jufeng Chen, Bo Zheng
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model
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Post-Training Attribute Unlearning in Recommender Systems arXiv.cs.IR Pub Date : 2024-03-11 Chaochao Chen, Yizhao Zhang, Yuyuan Li, Dan Meng, Jun Wang, Xiaoli Zheng, Jianwei Yin
With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as unlearning target. However, attackers can extract private information from the model even if it has not been explicitly encountered during training. We name this unseen information as \textit{attribute} and treat
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Emergency Response Inference Mapping (ERIMap): A Bayesian Network-based Method for Dynamic Observation Processing in Spatially Distributed Emergencies arXiv.cs.IR Pub Date : 2024-03-11 Moritz Schneider, Lukas Halekotte, Tina Comes, Daniel Lichte, Frank Fiedrich
In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in decisions. Currently, there is a lack of systematic approaches
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ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval arXiv.cs.IR Pub Date : 2024-03-11 Yuanhang Zheng, Peng Li, Wei Liu, Yang Liu, Jian Luan, Bin Wang
Tool learning aims to extend the capabilities of large language models (LLMs) with external tools. A major challenge in tool learning is how to support a large number of tools, including unseen tools. To address this challenge, previous studies have proposed retrieving suitable tools for the LLM based on the user query. However, previously proposed methods do not consider the differences between seen
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RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems arXiv.cs.IR Pub Date : 2024-03-11 Jianxun Lian, Yuxuan Lei, Xu Huang, Jing Yao, Wei Xu, Xing Xie
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI provides a suite of tools, including Recommender AI Agent, Recommendation-oriented Language Models, Knowledge Plugin, RecExplainer, and Evaluator, to facilitate the integration of LLMs into recommender systems from multifaceted
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CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation arXiv.cs.IR Pub Date : 2024-03-11 Junda Wu, Cheng-Chun Chang, Tong Yu, Zhankui He, Jianing Wang, Yupeng Hou, Julian McAuley
The long-tail recommendation is a challenging task for traditional recommender systems, due to data sparsity and data imbalance issues. The recent development of large language models (LLMs) has shown their abilities in complex reasoning, which can help to deduce users' preferences based on very few previous interactions. However, since most LLM-based systems rely on items' semantic meaning as the
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Repeated Padding as Data Augmentation for Sequential Recommendation arXiv.cs.IR Pub Date : 2024-03-11 Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Jianzhe Zhao
Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences; 2) Batching-based training needs to ensure that the sequences in each batch have the same length. The special value \emph{0}
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CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System arXiv.cs.IR Pub Date : 2024-03-08 Yashar Deldjoo, Tommaso di Noia
In the evolving landscape of recommender systems, the integration of Large Language Models (LLMs) such as ChatGPT marks a new era, introducing the concept of Recommendation via LLM (RecLLM). While these advancements promise unprecedented personalization and efficiency, they also bring to the fore critical concerns regarding fairness, particularly in how recommendations might inadvertently perpetuate
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Leveraging Foundation Models for Content-Based Medical Image Retrieval in Radiology arXiv.cs.IR Pub Date : 2024-03-11 Stefan Denner, David Zimmerer, Dimitrios Bounias, Markus Bujotzek, Shuhan Xiao, Lisa Kausch, Philipp Schader, Tobias Penzkofer, Paul F. Jäger, Klaus Maier-Hein
Content-based image retrieval (CBIR) has the potential to significantly improve diagnostic aid and medical research in radiology. Current CBIR systems face limitations due to their specialization to certain pathologies, limiting their utility. In response, we propose using vision foundation models as powerful and versatile off-the-shelf feature extractors for content-based medical image retrieval.
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Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery arXiv.cs.IR Pub Date : 2024-03-10 Yuxuan Yao, Sichun Luo, Haohan Zhao, Guanzhi Deng, Linqi Song
We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{V}ehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER models. To construct this dataset, we sourced
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Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing arXiv.cs.IR Pub Date : 2024-03-10 Liyang He, Zhenya Huang, Jiayu Liu, Enhong Chen, Fei Wang, Jing Sha, Shijin Wang
Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult
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Aligning Large Language Models for Controllable Recommendations arXiv.cs.IR Pub Date : 2024-03-08 Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie
Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable. However, existing literature primarily concentrates on integrating domain-specific knowledge into LLMs to enhance accuracy, often neglecting the
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ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data arXiv.cs.IR Pub Date : 2024-03-07 Liana Patel, Peter Kraft, Carlos Guestrin, Matei Zaharia
Applications increasingly leverage mixed-modality data, and must jointly search over vector data, such as embedded images, text and video, as well as structured data, such as attributes and keywords. Proposed methods for this hybrid search setting either suffer from poor performance or support a severely restricted set of search predicates (e.g., only small sets of equality predicates), making them
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Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering arXiv.cs.IR Pub Date : 2024-03-08 Hongda Sun, Yuxuan Liu, Chengwei Wu, Haiyu Yan, Cheng Tai, Xin Gao, Shuo Shang, Rui Yan
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems. Existing methods follow two main paradigms to collect evidence: (1) The \textit{retrieve-then-read} paradigm retrieves pertinent documents from an external corpus; and (2) the \textit{generate-then-read} paradigm employs large language models (LLMs) to generate relevant documents. However, neither
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Benchmarking News Recommendation in the Era of Green AI arXiv.cs.IR Pub Date : 2024-03-07 Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiao-Ming Wu
Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems. Concurrently, Green AI advocates for reducing the energy consumption and environmental impact of machine learning. To address these concerns, we introduce the first Green AI benchmarking
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Ducho 2.0: Towards a More Up-to-Date Feature Extraction and Processing Framework for Multimodal Recommendation arXiv.cs.IR Pub Date : 2024-03-07 Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia
In this work, we introduce Ducho 2.0, the latest stable version of our framework. Differently from Ducho, Ducho 2.0 offers a more personalized user experience with the definition and import of custom extraction models fine-tuned on specific tasks and datasets. Moreover, the new version is capable of extracting and processing features through multimodal-by-design large models. Notably, all these new
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The 2nd Workshop on Recommendation with Generative Models arXiv.cs.IR Pub Date : 2024-03-07 Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong Liu, Xiangyu Zhao, Wayne Xin Zhao, Yang Song, Xiangnan He
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender
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DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives arXiv.cs.IR Pub Date : 2024-03-07 Leilei Ding, Dazhong Shen, Chao Wang, Tianfu Wang, Le Zhang, Hui Xiong, Yanyong Zhang
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node information and topology. However, these models often face the famous over-smoothing issue, leading to indistinct user and item embeddings and reduced personalization. Traditional desmoothing methods in GCN-based systems are model-specific
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SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation arXiv.cs.IR Pub Date : 2024-03-07 Chi Zhang, Qilong Han, Rui Chen, Xiangyu Zhao, Peng Tang, Hongtao Song
Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored modeling sequentiality and correlations in
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Towards Robustness Analysis of E-Commerce Ranking System arXiv.cs.IR Pub Date : 2024-03-07 Ningfei Wang, Yupin Huang, Han Cheng, Jiri Gesi, Xiaojie Wang, Vivek Mittal
Information retrieval (IR) is a pivotal component in various applications. Recent advances in machine learning (ML) have enabled the integration of ML algorithms into IR, particularly in ranking systems. While there is a plethora of research on the robustness of ML-based ranking systems, these studies largely neglect commercial e-commerce systems and fail to establish a connection between real-world
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ALTO: An Efficient Network Orchestrator for Compound AI Systems arXiv.cs.IR Pub Date : 2024-03-07 Keshav Santhanam, Deepti Raghavan, Muhammad Shahir Rahman, Thejas Venkatesh, Neha Kunjal, Pratiksha Thaker, Philip Levis, Matei Zaharia
We present ALTO, a network orchestrator for efficiently serving compound AI systems such as pipelines of language models. ALTO achieves high throughput and low latency by taking advantage of an optimization opportunity specific to generative language models: streaming intermediate outputs. As language models produce outputs token by token, ALTO exposes opportunities to stream intermediate outputs between
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Backtracing: Retrieving the Cause of the Query arXiv.cs.IR Pub Date : 2024-03-06 Rose E. Wang, Pawan Wirawarn, Omar Khattab, Noah Goodman, Dorottya Demszky
Many online content portals allow users to ask questions to supplement their understanding (e.g., of lectures). While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators -- such as lecturers who want to improve their content -- identify segments that _caused_ a user to ask those questions. We introduce the task of backtracing, in
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Bridging Language and Items for Retrieval and Recommendation arXiv.cs.IR Pub Date : 2024-03-06 Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, Julian McAuley
This paper introduces BLaIR, a series of pretrained sentence embedding models specialized for recommendation scenarios. BLaIR is trained to learn correlations between item metadata and potential natural language context, which is useful for retrieving and recommending items. To pretrain BLaIR, we collect Amazon Reviews 2023, a new dataset comprising over 570 million reviews and 48 million items from
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Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models arXiv.cs.IR Pub Date : 2024-03-06 Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, James Caverlee
Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven to be effective for sequential recommendation, they suffer from the inference inefficiency problem stemming from the quadratic computational complexity of attention operators, especially for long-range behavior sequences.
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Intent-aware Recommendation via Disentangled Graph Contrastive Learning arXiv.cs.IR Pub Date : 2024-03-06 Yuling Wang, Xiao Wang, Xiangzhou Huang, Yanhua Yu, Haoyang Li, Mengdi Zhang, Zirui Guo, Wei Wu
Graph neural network (GNN) based recommender systems have become one of the mainstream trends due to the powerful learning ability from user behavior data. Understanding the user intents from behavior data is the key to recommender systems, which poses two basic requirements for GNN-based recommender systems. One is how to learn complex and diverse intents especially when the user behavior is usually
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Towards Efficient and Effective Unlearning of Large Language Models for Recommendation arXiv.cs.IR Pub Date : 2024-03-06 Hangyu Wang, Jianghao Lin, Bo Chen, Yang Yang, Ruiming Tang, Weinan Zhang, Yong Yu
The significant advancements in large language models (LLMs) give rise to a promising research direction, i.e., leveraging LLMs as recommenders (LLMRec). The efficacy of LLMRec arises from the open-world knowledge and reasoning capabilities inherent in LLMs. LLMRec acquires the recommendation capabilities through instruction tuning based on user interaction data. However, in order to protect user privacy
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Generative News Recommendation arXiv.cs.IR Pub Date : 2024-03-06 Shen Gao, Jiabao Fang, Quan Tu, Zhitao Yao, Zhumin Chen, Pengjie Ren, Zhaochun Ren
Most existing news recommendation methods tackle this task by conducting semantic matching between candidate news and user representation produced by historical clicked news. However, they overlook the high-level connections among different news articles and also ignore the profound relationship between these news articles and users. And the definition of these methods dictates that they can only deliver
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Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling arXiv.cs.IR Pub Date : 2024-03-06 Chao-Wei Huang, Chen-An Li, Tsu-Yuan Hsu, Chen-Yu Hsu, Yun-Nung Chen
Dense retrieval methods have demonstrated promising performance in multilingual information retrieval, where queries and documents can be in different languages. However, dense retrievers typically require a substantial amount of paired data, which poses even greater challenges in multilingual scenarios. This paper introduces UMR, an Unsupervised Multilingual dense Retriever trained without any paired
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Contrastive Pre-training for Deep Session Data Understanding arXiv.cs.IR Pub Date : 2024-03-05 Zixuan Li, Lizi Liao, Yunshan Ma, Tat-Seng Chua
Session data has been widely used for understanding user's behavior in e-commerce. Researchers are trying to leverage session data for different tasks, such as purchase intention prediction, remaining length prediction, recommendation, etc., as it provides context clues about the user's dynamic interests. However, online shopping session data is semi-structured and complex in nature, which contains
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Learning to Ask Critical Questions for Assisting Product Search arXiv.cs.IR Pub Date : 2024-03-05 Zixuan Li, Lizi Liao, Tat-Seng Chua
Product search plays an essential role in eCommerce. It was treated as a special type of information retrieval problem. Most existing works make use of historical data to improve the search performance, which do not take the opportunity to ask for user's current interest directly. Some session-aware methods take the user's clicks within the session as implicit feedback, but it is still just a guess
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Uplift Modeling for Target User Attacks on Recommender Systems arXiv.cs.IR Pub Date : 2024-03-05 Wenjie Wang, Changsheng Wang, Fuli Feng, Wentao Shi, Daizong Ding, Tat-Seng Chua
Recommender systems are vulnerable to injective attacks, which inject limited fake users into the platforms to manipulate the exposure of target items to all users. In this work, we identify that conventional injective attackers overlook the fact that each item has its unique potential audience, and meanwhile, the attack difficulty across different users varies. Blindly attacking all users will result
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ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary arXiv.cs.IR Pub Date : 2024-03-05 Yutong Li, Lu Chen, Aiwei Liu, Kai Yu, Lijie Wen
The literature review is an indispensable step in the research process. It provides the benefit of comprehending the research problem and understanding the current research situation while conducting a comparative analysis of prior works. However, literature summary is challenging and time consuming. The previous LLM-based studies on literature review mainly focused on the complete process, including
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CODE-ACCORD: A Corpus of Building Regulatory Data for Rule Generation towards Automatic Compliance Checking arXiv.cs.IR Pub Date : 2024-03-04 Hansi Hettiarachchi, Amna Dridi, Mohamed Medhat Gaber, Pouyan Parsafard, Nicoleta Bocaneala, Katja Breitenfelder, Gonçal Costa, Maria Hedblom, Mihaela Juganaru-Mathieu, Thamer Mecharnia, Sumee Park, He Tan, Abdel-Rahman H. Tawil, Edlira Vakaj
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. However, extracting information from textual rules to convert them to a machine-readable format has been a challenge due to the complexities associated with natural language and the limited resources
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Evaluating the Explainability of Neural Rankers arXiv.cs.IR Pub Date : 2024-03-04 Saran Pandian, Debasis Ganguly, Sean MacAvaney
Information retrieval models have witnessed a paradigm shift from unsupervised statistical approaches to feature-based supervised approaches to completely data-driven ones that make use of the pre-training of large language models. While the increasing complexity of the search models have been able to demonstrate improvements in effectiveness (measured in terms of relevance of top-retrieved results)