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 1951–1975 of 4740 papers

TitleStatusHype
Dynamic Difficulty Awareness Training for Continuous Emotion Prediction—0
Generalized Abbreviation Prediction with Negative Full Forms and Its Application on Improving Chinese Web Search—0
DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets—0
Generalized Relative Neighborhood Graph (GRNG) for Similarity Search—0
Generalized residual vector quantization for large scale data—0
Generalized Weak Supervision for Neural Information Retrieval—0
Generating and using probabilistic morphological resources for the biomedical domain—0
Generating Answering Patterns from Factoid Arabic Questions—0
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles—0
Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training—0
An Initial Test Collection for Ranked Retrieval of SMS Conversations—0
Generative Adversarial Networks for Multimodal Representation Learning in Video Hyperlinking—0
DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis—0
Duplicate Detection in a Knowledge Base with PIKA—0
Duplicate Bug Report Detection With a Combination of Information Retrieval and Topic Modeling—0
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
Generative Information Retrieval Evaluation—0
Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models—0
Generative Query Reformulation for Effective Adhoc Search—0
Generative Retrieval Meets Multi-Graded Relevance—0
Generative Semantic Hashing Enhanced via Boltzmann Machines—0
Generic decoding of seen and imagined objects using hierarchical visual features—0
Dudley North visits North London: Learning When to Transliterate to Arabic—0
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models—0
Aggregating Continuous Word Embeddings for Information Retrieval—0
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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