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 5301–5350 of 14297 papers

TitleStatusHype
Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations—0
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs—0
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages—0
FastRAG: Retrieval Augmented Generation for Semi-structured Data—0
Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documents—0
Comparing Traditional and LLM-based Search for Image Geolocation—0
FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation—0
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring—0
FinSage: A Multi-aspect RAG System for Financial Filings Question Answering—0
AI Revolution on Chat Bot: Evidence from a Randomized Controlled Experiment—0
ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring—0
Comparison of the effects of investor attention using search volume data before and after mobile device popularization—0
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential—0
AskMe: A LAPPS Grid-based NLP Query and Retrieval System for Covid-19 Literature—0
Firing Rate Models as Associative Memory: Excitatory-Inhibitory Balance for Robust Retrieval—0
First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1—0
First Steps towards the Semi-automatic Development of a Wordformation-based Lexicon of Latin—0
First Token Probability Guided RAG for Telecom Question Answering—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—0
Competitive Retrieval: Going Beyond the Single Query—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
Complementarity, F-score, and NLP Evaluation—0
Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval—0
Complementing Lexical Retrieval with Semantic Residual Embedding—0
FJWU Participation for the WMT21 Biomedical Translation Task—0
AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method—0
Graded Relevance Scoring of Written Essays with Dense Retrieval—0
FLAP: Fast Language-Audio Pre-training—0
FlashBack:Efficient Retrieval-Augmented Language Modeling for Long Context Inference—0
FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking—0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG—0
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research—0
Fast Metric Learning For Deep Neural Networks—0
Fast Loop Closure Detection via Binary Content—0
Fast Kronecker product kernel methods via generalized vec trick—0
Asking More Informative Questions for Grounded Retrieval—0
Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing—0
Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval—0
Fast Interactive Image Retrieval using large-scale unlabeled data—0
FastHybrid: A Hybrid Model for Efficient Answer Selection—0
Fast Gumbel-Max Sketch and its Applications—0
Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization—0
COIN: Co-Cluster Infomax for Bipartite Graphs—0
GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback—0
Fast Generating A Large Number of Gumbel-Max Variables—0
Asking Clarifying Questions Based on Negative Feedback in Conversational Search—0
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks—0
Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach—0
GrabQC: Graph based Query Contextualization for automated ICD coding—0
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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