Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study
While content-based image retrieval (CBIR) has been extensively studied in natural image retrieval, its application to medical images presents ongoing challenges, primarily due to the 3D nature of medical images. Recent …
Content-Based Image RetrievalImage RetrievalRe-RankingRetrieval+1Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking
Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, distilled models do not match the effectiveness of their te…
Language ModellingLarge Language ModelPassage Re-RankingRe-RankingFull Stage Learning to Rank: A Unified Framework for Multi-Stage Systems
The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems. The principle requires an IR module's returned list of results to be ranked wi…
Information RetrievalLearning-To-RankRe-RankingRetrieval+1Technical Report of NICE Challenge at CVPR 2024: Caption Re-ranking Evaluation Using Ensembled CLIP and Consensus Scores
This report presents the ECO (Ensembled Clip score and cOnsensus score) pipeline from team DSBA LAB, which is a new framework used to evaluate and rank captions for a given image. ECO selects the most accurate caption de…
Image CaptioningRe-RankingGenerative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023
This paper describes our participation in the TREC 2023 Deep Learning Track. We submitted runs that apply generative relevance feedback from a large language model in both a zero-shot and pseudo-relevance feedback settin…
Language ModelingLanguage ModellingLarge Language ModelRe-Ranking+1PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
Utilizing large language models (LLMs) for zero-shot document ranking is done in one of two ways: (1) prompt-based re-ranking methods, which require no further training but are only feasible for re-ranking a handful of c…
Document RankingRe-RankingRetrievalRanked List Truncation for Large Language Model-based Re-Ranking
We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank" perspective, where we optimize re-ranking by truncating the retrieved list (i.e., trim re-ranking candidates). RLT is crucial for re-ranking as i…
Language ModelingLanguage ModellingLarge Language ModelRe-Ranking+1A Taxation Perspective for Fair Re-ranking
Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure responsibility and ethics. The exploration of redistribution problems has a long history in economics, offering valuable insights fo…
EthicsFairnessRe-RankingA Reproducibility Study of PLAID
The PLAID (Performance-optimized Late Interaction Driver) algorithm for ColBERTv2 uses clustered term representations to retrieve and progressively prune documents for final (exact) document scoring. In this paper, we re…
Re-RankingRetrievalLLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) demonstrates great value in alleviating outdated knowledge or hallucination by supplying LLMs with updated and relevant knowledge. However, there are still several difficulties for RA…
HallucinationRAGRe-RankingRetrieval+2SPLATE: Sparse Late Interaction Retrieval
The late interaction paradigm introduced with ColBERT stands out in the neural Information Retrieval space, offering a compelling effectiveness-efficiency trade-off across many benchmarks. Efficient late interaction retr…
CPUInformation RetrievalRe-RankingRetrievalIs Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval
Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previous methods assume the answer to such a q…
Open-Domain Question AnsweringQuestion AnsweringRe-RankingRetrieval+1The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the \textit{\textbf{structural knowledge}} in the local neighborhood of e…
Entity TypingKnowledge GraphsRe-RankingSet-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Existing cross-encoder models can be categorized as pointwise, pairwise, or listwise. Pairwise and listwise models allow passage interactions, which typically makes them more effective than pointwise models but less effi…
Passage Re-RankingRe-RankingClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish
Advances in natural language processing techniques, such as named entity recognition and normalization to widely used standardized terminologies like UMLS or SNOMED-CT, along with the digitalization of electronic health …
Contrastive LearningEntity Linkingnamed-entity-recognitionNamed Entity Recognition+1Multi-Granularity Guided Fusion-in-Decoder
In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results. The model architecture that uses concatenated multiple contexts in the d…
DecoderMulti-Task LearningNatural QuestionsOpen-Domain Question Answering+6LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations
The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models (LLMs) can model …
Recommendation SystemsRe-RankingWorld KnowledgeKC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate.…
Knowledge Graph CompletionRe-RankingEnhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models
Large language models with billions of parameters, such as GPT-3.5, GPT-4, and LLaMA, are increasingly prevalent. Numerous studies have explored effective prompting techniques to harness the power of these LLMs for vario…
ArticlesRe-RankingRetrievalImproving Retrieval for RAG based Question Answering Models on Financial Documents
The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhance…
ChunkingQuestion AnsweringRAGRe-Ranking+3