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Showing 226–250 of 743 papers

TitleStatusHype
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval—0
Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking—0
Cheb-GR: Rethinking K-nearest Neighbor Search in Re-ranking for Person Re-identification—0
Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems—0
English-Korean Named Entity Transliteration Using Substring Alignment and Re-ranking Methods—0
Document Retrieval and Claim Verification to Mitigate COVID-19 Misinformation—0
Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?—0
Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models—0
Enhancing Recommendation Diversity by Re-ranking with Large Language Models—0
A Sentence Is Worth a Thousand Pixels—0
Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications—0
Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking—0
Ensuring User-side Fairness in Dynamic Recommender Systems—0
Divide and Fuse: A Re-ranking Approach for Person Re-identification—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
Entity Summarization: State of the Art and Future Challenges—0
Diverse Multi-Answer Retrieval with Determinantal Point Processes—0
Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking—0
Coarse-grained Candidate Generation and Fine-grained Re-ranking for Chinese Abbreviation Prediction—0
Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets—0
Evaluating Embedding APIs for Information Retrieval—0
A Free Format Legal Question Answering System—0
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models—0
GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
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