SOTAVerified

Information Retrieval

Information retrieval is the task of ranking a list of documents or search results in response to a query

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Papers

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Showing 1326–1350 of 4740 papers

TitleStatusHype
An Empirical Comparison of LM-based Question and Answer Generation Methods—0
People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts—0
DataFinder: Scientific Dataset Recommendation from Natural Language DescriptionsCode1
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning—0
Fusion-in-T5: Unifying Document Ranking Signals for Improved Information RetrievalCode0
C-STS: Conditional Semantic Textual SimilarityCode1
Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and BenchmarkCode0
Referral Augmentation for Zero-Shot Information RetrievalCode0
Knowledge Graphs Querying—0
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on WikipediaCode3
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-rankerCode0
BM25 Query Augmentation Learned End-to-End—0
Generative Retrieval via Term Set GenerationCode1
Conversational Recommendation as Retrieval: A Simple, Strong Baseline—0
Differential Privacy with Random Projections and Sign Random Projections—0
REFinD: Relation Extraction Financial DatasetCode0
IR Models and the COVID-19 Pandemic: A Comparative Study of Performance and Challenges—0
Exploring the Viability of Synthetic Query Generation for Relevance Prediction—0
CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search—0
How Does Generative Retrieval Scale to Millions of Passages?—0
ReFIT: Relevance Feedback from a Reranker during InferenceCode1
Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage ResolutionCode0
Seq-HGNN: Learning Sequential Node Representation on Heterogeneous GraphCode1
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval—0
Query Performance Prediction: From Ad-hoc to Conversational SearchCode1
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All datasetsBSARDMS MARCOCQADupStackTREC-PMAmazonMSLR WEB30KMSMARCOMTEBNews HeadlinesOhsumed

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Two-tower Bi-Encoder (RoBERTa)Recall@10074.78—Unverified
2Siamese Bi-Encoder (RoBERTa)Recall@10071.63—Unverified
3BM25Recall@10051.33—Unverified
#ModelMetricClaimedVerifiedStatus
1RetroMAE v2MRR@1042.58—Unverified
2ConAE-256Time (ms)0.33—Unverified
3ConAE-128Time (ms)0.32—Unverified
#ModelMetricClaimedVerifiedStatus
1SGPT-BE-5.8BmAP@1000.16—Unverified
2TSDAEmAP@1000.15—Unverified
#ModelMetricClaimedVerifiedStatus
1hpipubcommoninfNDCG0.56—Unverified
2hpictallinfNDCG0.55—Unverified
#ModelMetricClaimedVerifiedStatus
1MINDHR@300.32—Unverified
#ModelMetricClaimedVerifiedStatus
1Distilled NetworknDCG@100.53—Unverified
#ModelMetricClaimedVerifiedStatus
1RetroMAEMRR@100.42—Unverified
#ModelMetricClaimedVerifiedStatus
1SGPT-5.8B-msmarconDCG@1050.25—Unverified
#ModelMetricClaimedVerifiedStatus
1Information Retrieval + SVM1:1 Accuracy83.79—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT+CONCEPT FILTERNDCG0.25—Unverified