Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems
Enterprise search systems often struggle to retrieve accurate, domain-specific information due to semantic mismatches and overlapping terminologies. These issues can degrade the performance of downstream applications suc…
Computational EfficiencyDimensionality ReductionManagementRe-Ranking+2Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains
Traditional Retrieval-Augmented Generation (RAG) pipelines rely on similarity-based retrieval and re-ranking, which depend on heuristics such as top-k, and lack explainability, interpretability, and robustness against ad…
RAGRe-RankingRetrievalRetrieval-augmented GenerationR2MED: A Benchmark for Reasoning-Driven Medical Retrieval
Current medical retrieval benchmarks primarily emphasize lexical or shallow semantic similarity, overlooking the reasoning-intensive demands that are central to clinical decision-making. In practice, physicians often ret…
DiagnosticRe-RankingRetrievalSemantic Similarity+1Focus, Merge, Rank: Improved Question Answering Based on Semi-structured Knowledge Bases
In many real-world settings, machine learning models and interactive systems have access to both structured knowledge, e.g., knowledge graphs or tables, and unstructured content, e.g., natural language documents. However…
Knowledge GraphsMulti-hop Question AnsweringQuestion AnsweringRe-RankingOptimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency
Large language models achieve high task performance yet often hallucinate or rely on outdated knowledge. Retrieval-augmented generation (RAG) addresses these gaps by coupling generation with external search. We analyse h…
ChunkingRAGRe-RankingRetrieval+2A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios
Cross-view geo-localization is a promising solution for large-scale localization problems, requiring the sequential execution of retrieval and metric localization tasks to achieve fine-grained predictions. However, exist…
geo-localizationRe-RankingRetrievalA Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao
E-commerce recommendation systems aim to generate ordered lists of items for customers, optimizing multiple business objectives, such as clicks, conversions and Gross Merchandise Volume (GMV). Traditional multi-objective…
DiversityLearning-To-RankRecommendation Systemsregression+1MedEIR: A Specialized Medical Embedding Model for Enhanced Information Retrieval
Embedding models have become essential for retrieval-augmented generation (RAG) tasks, semantic clustering, and text re-ranking. But despite their growing use, many of these come with notable limitations. For example, Ji…
Information RetrievalRAGRe-RankingRetrieval+1VR-RAG: Open-vocabulary Species Recognition with RAG-Assisted Large Multi-Modal Models
Open-vocabulary recognition remains a challenging problem in computer vision, as it requires identifying objects from an unbounded set of categories. This is particularly relevant in nature, where new species are discove…
ArticlesRAGRe-RankingRetrieval+1Theoretical Guarantees for LT-TTD: A Unified Transformer-based Architecture for Two-Level Ranking Systems
Modern recommendation and search systems typically employ multi-stage ranking architectures to efficiently handle billions of candidates. The conventional approach uses distinct L1 (candidate retrieval) and L2 (re-rankin…
Computational EfficiencyKnowledge DistillationRe-RankingRetrievalInvestigating Task Arithmetic for Zero-Shot Information Retrieval
Large Language Models (LLMs) have shown impressive zero-shot performance across a variety of Natural Language Processing tasks, including document re-ranking. However, their effectiveness degrades on unseen tasks and dom…
Information RetrievalRe-RankingRetrievalTask Arithmetic+1Scalable Unit Harmonization in Medical Informatics Using Bi-directional Transformers and Bayesian-Optimized BM25 and Sentence Embedding Retrieval
Objective: To develop and evaluate a scalable methodology for harmonizing inconsistent units in large-scale clinical datasets, addressing a key barrier to data interoperability. Materials and Methods: We designed a novel…
Bayesian OptimizationInformation RetrievalRe-RankingRetrieval+4TartuNLP at SemEval-2025 Task 5: Subject Tagging as Two-Stage Information Retrieval
We present our submission to the Task 5 of SemEval-2025 that aims to aid librarians in assigning subject tags to the library records by producing a list of likely relevant tags for a given document. We frame the task as …
Information RetrievalRe-RankingRetrievalAre Information Retrieval Approaches Good at Harmonising Longitudinal Survey Questions in Social Science?
Automated detection of semantically equivalent questions in longitudinal social science surveys is crucial for long-term studies informing empirical research in the social, economic, and health sciences. Retrieving equiv…
Information RetrievalRe-RankingSurveyMATCHA: Can Multi-Agent Collaboration Build a Trustworthy Conversational Recommender?
In this paper, we propose a multi-agent collaboration framework called MATCHA for conversational recommendation system, leveraging large language models (LLMs) to enhance personalization and user engagement. Users can re…
Conversational RecommendationDiversityRecommendation SystemsRe-RankingFinBERT-QA: Financial Question Answering with pre-trained BERT Language Models
Motivated by the emerging demand in the financial industry for the automatic analysis of unstructured and structured data at scale, Question Answering (QA) systems can provide lucrative and competitive advantages to comp…
Answer SelectionInformation RetrievalQuestion AnsweringRe-Ranking+2Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics
Fairness is an increasingly important factor in re-ranking tasks. Prior work has identified a trade-off between ranking accuracy and item fairness. However, the underlying mechanisms are still not fully understood. An an…
FairnessRe-RankingRegret-aware Re-ranking for Guaranteeing Two-sided Fairness and Accuracy in Recommender Systems
In multi-stakeholder recommender systems (RS), users and providers operate as two crucial and interdependent roles, whose interests must be well-balanced. Prior research, including our work BankFair, has demonstrated the…
FairnessRecommendation SystemsRe-RankingFinSage: A Multi-aspect RAG System for Financial Filings Question Answering
Leveraging large language models in real-world settings often entails a need to utilize domain-specific data and tools in order to follow the complex regulations that need to be followed for acceptable use. Within financ…
Question AnsweringRAGRe-RankingRetrieval+1Optimizing Compound Retrieval Systems
Modern retrieval systems do not rely on a single ranking model to construct their rankings. Instead, they generally take a cascading approach where a sequence of ranking models are applied in multiple re-ranking stages. …
Information RetrievalRe-RankingRetrieval