SOTAVerified

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 5501–5525 of 14297 papers

TitleStatusHype
IR Models and the COVID-19 Pandemic: A Comparative Study of Performance and Challenges—0
Direct Fact Retrieval from Knowledge Graphs without Entity Linking—0
Description-Based Text Similarity—0
Automated Few-shot Classification with Instruction-Finetuned Language ModelsCode0
Mol-PECO: a deep learning model to predict human olfactory perception from molecular structures—0
Knowledge-Design: Pushing the Limit of Protein Design via Knowledge RefinementCode2
Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set AlignmentCode1
Patton: Language Model Pretraining on Text-Rich Networks—0
Evaluation of medium-large Language Models at zero-shot closed book generative question answering—0
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain SettingsCode1
Searching by Code: a New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets—0
ReFIT: Relevance Feedback from a Reranker during InferenceCode1
How Does Generative Retrieval Scale to Millions of Passages?—0
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction—0
Complex Claim Verification with Evidence Retrieved in the WildCode1
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations—0
CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search—0
Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place RecognitionCode1
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach—0
Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings—0
Decouple knowledge from parameters for plug-and-play language modelingCode1
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense CaptionerCode1
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question AnsweringCode1
MALM: Mask Augmentation based Local Matching for Food-Recipe RetrievalCode0
Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage ResolutionCode0
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All datasetsQuora Question PairsHotpotQANatural QuestionsOK-VQAInfoSeekMVKPolyvorePubMedQAPubMedQA corpus with metadataToolLensคลิปไวรัล!! ไอซ์ ปรีชญา ลืมปิดไลฟ์สดตอนอาบน้ำ ถูกแชร์กระหึ่มเน็ต

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