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Retrieval

A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.

Papers

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Showing 201–250 of 14297 papers

TitleStatusHype
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge BasesCode3
InPars-v2: Large Language Models as Efficient Dataset Generators for Information RetrievalCode2
Benchmarking Retrieval-Augmented Generation in Multi-Modal ContextsCode2
InPars: Data Augmentation for Information Retrieval using Large Language ModelsCode2
InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information RetrievalCode2
INQUIRE: A Natural World Text-to-Image Retrieval BenchmarkCode2
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language ModelsCode2
Improving Diffusion Inverse Problem Solving with Decoupled Noise AnnealingCode2
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningCode2
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph CompletionCode2
In-Context Retrieval-Augmented Language ModelsCode2
InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized RationalesCode2
HourVideo: 1-Hour Video-Language UnderstandingCode2
Hopfield Networks is All You NeedCode2
How do you know that? Teaching Generative Language Models to Reference Answers to Biomedical QuestionsCode2
Autonomous GIS: the next-generation AI-powered GISCode2
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLCode2
HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented GenerationCode2
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam GenerationCode2
Grounding Language Models to Images for Multimodal Inputs and OutputsCode2
Global Features are All You Need for Image Retrieval and RerankingCode2
GLAP: General contrastive audio-text pretraining across domains and languagesCode2
GiantMIDI-Piano: A large-scale MIDI dataset for classical piano musicCode2
GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook RetrievalCode2
Hello Again! LLM-powered Personalized Agent for Long-term DialogueCode2
Huatuo-26M, a Large-scale Chinese Medical QA DatasetCode2
Interactive Continual Learning: Fast and Slow ThinkingCode2
Generalized Contrastive Learning for Multi-Modal Retrieval and RankingCode2
Generating Benchmarks for Factuality Evaluation of Language ModelsCode2
GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical InformationCode2
Generating Images with Multimodal Language ModelsCode2
VeCLIP: Improving CLIP Training via Visual-enriched CaptionsCode2
FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity SearchCode2
FLAIR: VLM with Fine-grained Language-informed Image RepresentationsCode2
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question AnsweringCode2
Autoregressive Search Engines: Generating Substrings as Document IdentifiersCode2
Fine-grained Image Captioning with CLIP RewardCode2
Flow-Guided Transformer for Video InpaintingCode2
A Survey of Personalization: From RAG to AgentCode2
Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMsCode2
AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language ModelsCode2
Backtracing: Retrieving the Cause of the QueryCode2
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval ModelsCode2
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed RetrievalCode2
BEBLID: Boosted efficient binary local image descriptorCode2
A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information RetrievalCode2
Benchmarking Large Language Models in Retrieval-Augmented GenerationCode2
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open QuestionsCode2
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow InstructionsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53—Unverified
2ElasticsearchQueries per second21.8—Unverified
3BM25-PTQueries per second6.49—Unverified
4Rank-BM25Queries per second1.18—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88—Unverified
2ElasticsearchQueries per second7.11—Unverified
3Rank-BM25Queries per second0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85—Unverified
2ElasticsearchQueries per second12.16—Unverified
3Rank-BM25Queries per second0.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32—Unverified
2RA-VQARecall@582.84—Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55—Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222—Unverified