Papers Document Ranking
“Document Ranking” 태그가 달린 논문 177편 · 필터 해제
CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) depends on document ranking to provide useful evidence for generation, but conventional reranking methods mainly optimize query-document relevance rather than generation usefulness. A…
Document RankingCoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
Agentic search -- the task of training agents that iteratively reason, issue queries, and synthesize retrieved information to answer complex questions -- has achieved remarkable progress through reinforcement learning (R…
Reinforcement LearningDocument RankingBeyond Fluency: Toward Reliable Trajectories in Agentic IR
Information Retrieval is shifting from passive document ranking toward autonomous agentic workflows that operate in multi-step Reason-Act-Observe loops. In such long-horizon trajectories, minor early errors can cascade, …
Information RetrievalDocument RankingAnalytical Search
Analytical information needs, such as trend analysis and causal impact assessment, are prevalent across various domains including law, finance, science, and much more. However, existing information retrieval paradigms, w…
Information RetrievalQuestion AnsweringDocument RankingReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search
Retrieval-Augmented Generation (RAG) enhances Large Language Models by grounding their outputs in external documents. These systems, however, remain vulnerable to attacks on the retrieval corpus, such as prompt injection…
Adversarial RobustnessDocument Ranking+VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checking
Identification of appropriate supporting evidence is critical to the success of scientific fact checking. However, existing approaches rely on off-the-shelf Information Retrieval algorithms that rank documents based on r…
Information RetrievalDocument RankingFact CheckingOverview of the TREC 2023 deep learning track
This is the fifth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human-annotated training labels available for both passage and document r…
Document RankingPassage RankingOverview of the TREC 2022 deep learning track
This is the fourth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels available for both passage and document …
Passage RetrievalDocument RankingOverview of the TREC 2021 deep learning track
This is the third year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels available for both passage and document r…
Document RankingPrecise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Information
Recent advancements have successfully harnessed the power of Large Language Models (LLMs) for zero-shot document ranking, exploring a variety of prompting strategies. Comparative approaches like pairwise and listwise ach…
Document RankingBridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking
In the realm of information retrieval, users often engage in multi-turn interactions with search engines to acquire information, leading to the formation of sequences of user feedback behaviors. Leveraging the session co…
Document RankingInformation RetrievalKnowledge DistillationModel OptimizationBeyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering
Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances have improved overall performance, they often encounter difficulties in educationa…
Document RankingLarge Language ModelMultimodal Large Language ModelQuestion Answering+2A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
In the field of multi-document summarization (MDS), transformer-based models have demonstrated remarkable success, yet they suffer an input length limitation. Current methods apply truncation after the retrieval process …
Document RankingDocument SummarizationMulti-Document SummarizationRetrievalHow do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment generation. However, the internal mechanisms b…
Document RankingInformation RetrievalBeyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion
This study presents a comprehensive reproducibility and extension analysis of the Setwise prompting methodology for zero-shot ranking with Large Language Models (LLMs), as proposed by Zhuang et al. We evaluate its effect…
Computational EfficiencyDocument RankingRerankingDistillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
We present a novel approach for training small language models for reasoning-intensive document ranking that combines knowledge distillation with reinforcement learning optimization. While existing methods often rely on …
Document RankingInformation RetrievalKnowledge DistillationLanguage Modeling+4Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities
Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously e…
Document RankingGraph LearningGraph Representation LearningInformation Retrieval+6On Synthetic Data Strategies for Domain-Specific Generative Retrieval
This paper investigates synthetic data generation strategies in developing generative retrieval models for domain-specific corpora, thereby addressing the scalability challenges inherent in manually annotating in-domain …
Document RankingRetrievalSynthetic Data GenerationWhite Hat Search Engine Optimization using Large Language Models
We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
Document RankingBeyond Questions: Leveraging ColBERT for Keyphrase Search
While question-like queries are gaining popularity and search engines' users increasingly adopt them, keyphrase search has traditionally been the cornerstone of web search. This query type is also prevalent in specialise…
Document RankingRetrieval