Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers
Understanding labour market dynamics requires accurately identifying the skills required for and possessed by the workforce. Automation techniques are increasingly being developed to support this effort. However, automat…
Re-RankingEffective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking
Clinical trials (CTs) often fail due to inadequate patient recruitment. This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two…
Descriptivenamed-entity-recognitionNamed Entity RecognitionNegation+3Adaptive Latent Entity Expansion for Document Retrieval
Despite considerable progress in neural relevance ranking techniques, search engines still struggle to process complex queries effectively - both in terms of precision and recall. Sparse and dense Pseudo-Relevance Feedba…
Re-RankingRetrievalEnhancing Documents with Multidimensional Relevance Statements in Cross-encoder Re-ranking
In this paper, we propose a novel approach to consider multiple dimensions of relevance beyond topicality in cross-encoder re-ranking. On the one hand, current multidimensional retrieval models often use na\"ive solution…
Re-RankingRetrievalOnline Distillation for Pseudo-Relevance Feedback
Model distillation has emerged as a prominent technique to improve neural search models. To date, distillation taken an offline approach, wherein a new neural model is trained to predict relevance scores between arbitrar…
Re-RankingRetrievalGraph Convolution Based Efficient Re-Ranking for Visual Retrieval
Visual retrieval tasks such as image retrieval and person re-identification (Re-ID) aim at effectively and thoroughly searching images with similar content or the same identity. After obtaining retrieved examples, re-ran…
Distributed ComputingImage RetrievalPerson Re-IdentificationRe-Ranking+1Distillation Strategies for Discriminative Speech Recognition Rescoring
Second-pass rescoring is employed in most state-of-the-art speech recognition systems. Recently, BERT based models have gained popularity for re-ranking the n-best hypothesis by exploiting the knowledge from masked langu…
Language ModelingLanguage ModellingRe-Rankingspeech-recognition+1Self-Enhancement Improves Text-Image Retrieval in Foundation Visual-Language Models
The emergence of cross-modal foundation models has introduced numerous approaches grounded in text-image retrieval. However, on some domain-specific retrieval tasks, these models fail to focus on the key attributes requi…
AttributeImage RetrievalRe-RankingRetrievalControllable Multi-Objective Re-ranking with Policy Hypernetworks
Multi-stage ranking pipelines have become widely used strategies in modern recommender systems, where the final stage aims to return a ranked list of items that balances a number of requirements such as user preference, …
Recommendation SystemsRe-RankingSciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation
Scientific writing involves retrieving, summarizing, and citing relevant papers, which can be time-consuming processes in large and rapidly evolving fields. By making these processes inter-operable, natural language proc…
Re-RankingSentenceTowards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning
Variances in ad impression outcomes across demographic groups are increasingly considered to be potentially indicative of algorithmic bias in personalized ads systems. While there are many definitions of fairness that co…
FairnessPrivacy Preservingreinforcement-learningRe-RankingBEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language
The BEIR dataset is a large, heterogeneous benchmark for Information Retrieval (IR) in zero-shot settings, garnering considerable attention within the research community. However, BEIR and analogous datasets are predomin…
Information RetrievalRe-RankingRetrievalTReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition
Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-based place recognition to find revisite…
3D Place RecognitionAutonomous DrivingInformation RetrievalLoop Closure Detection+2Generating EDU Extracts for Plan-Guided Summary Re-Ranking
Two-step approaches, in which summary candidates are generated-then-reranked to return a single summary, can improve ROUGE scores over the standard single-step approach. Yet, standard decoding methods (i.e., beam search,…
Language ModellingRe-RankingCandidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder
Composed image retrieval aims to find an image that best matches a given multi-modal user query consisting of a reference image and text pair. Existing methods commonly pre-compute image embeddings over the entire corpus…
Composed Image Retrieval (CoIR)Image RetrievalRerankingRe-Ranking+2Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models
Augmenting language models with a retrieval mechanism has been shown to significantly improve their performance while keeping the number of parameters low. Retrieval-augmented models commonly rely on a semantic retrieval…
Re-RankingRetrievalSemantic RetrievalAdapting Language Models to Compress Contexts
Transformer-based language models (LMs) are powerful and widely-applicable tools, but their usefulness is constrained by a finite context window and the expensive computational cost of processing long text documents. We …
In-Context LearningLanguage ModelingLanguage ModellingPassage Re-Ranking+2Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker
Re-rankers, which order retrieved documents with respect to the relevance score on the given query, have gained attention for the information retrieval (IR) task. Rather than fine-tuning the pre-trained language model (P…
Information RetrievalLanguage ModelingLanguage ModellingRe-Ranking+1Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding
Conversational AI systems such as Alexa need to understand defective queries to ensure robust conversational understanding and reduce user friction. These defective queries often arise from user ambiguities, mistakes, or…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Collaborative FilteringFriction+8Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed products, users tend to browse a large number …
DiversityGraph ClusteringRecommendation SystemsRe-Ranking