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Reranking

Papers

Showing 2650 of 586 papers

TitleStatusHype
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language ModelsCode2
INQUIRE: A Natural World Text-to-Image Retrieval BenchmarkCode2
EnCLAP++: Analyzing the EnCLAP Framework for Optimizing Automated Audio Captioning PerformanceCode2
LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document RerankingCode2
Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder ModelsCode2
Improving Bilingual Lexicon Induction with Cross-Encoder RerankingCode1
2nd Place Solution to Google Landmark Retrieval 2021Code1
Enhancing Mobile "How-to" Queries with Automated Search Results Verification and RerankingCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR DecompositionCode1
HypR: A comprehensive study for ASR hypothesis revising with a reference corpusCode1
Constructing and Evaluating Declarative RAG Pipelines in PyTerrierCode1
AcuRank: Uncertainty-Aware Adaptive Computation for Listwise RerankingCode1
Contrastive Learning of Sentence Embeddings from ScratchCode1
2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019Code1
Conformer-Kernel with Query Term Independence for Document RetrievalCode1
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward HackingCode1
HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer RerankingCode1
Instance-level Image Retrieval using Reranking TransformersCode1
Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked ClaimsCode1
Generating Scientific Definitions with Controllable ComplexityCode1
Generative Flow Network for Listwise RecommendationCode1
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data DiversityCode1
Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal EncoderCode1
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language ModelsCode1
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