Information Retrieval
843 papers with code • 10 benchmarks • 82 datasets
Information retrieval is the task of ranking a list of documents or search results in response to a query
( Image credit: sudhanshumittal )
Benchmarks
These leaderboards are used to track progress in Information Retrieval
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Libraries
Use these libraries to find Information Retrieval models and implementations
MS MARCO
BioASQ
CORD-19
PubLayNet
QASC
MTEB
MedleyDB
Subtasks
Latest papers
From Matching to Generation: A Survey on Generative Information Retrieval
We will summarize the advancements in GR regarding model training, document identifier, incremental learning, downstream tasks adaptation, multi-modal GR and generative recommendation, as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, generating response with citations and personal information assistant.
Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding
hansizeng/pag
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This paper introduces PAG-a novel optimization and decoding approach that guides autoregressive generation of document identifiers in generative retrieval models through simultaneous decoding.
MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering
Hence, to address this challenge, we also present a generic approach for translating SQuAD into any low-resource language.
Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models
With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift.
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search
Its objective is to return a set of $k$ data points that are closest to a query point, with its accuracy measured by the proportion of exact nearest neighbors captured in the returned set.
Spiral of Silences: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent.
VDTuner: Automated Performance Tuning for Vector Data Management Systems
However, due to the inherent characteristics of VDMS, automatic performance tuning for VDMS faces several critical challenges, which cannot be well addressed by the existing auto-tuning methods.
Lightweight Multi-System Multivariate Interconnection and Divergence Discovery
Identifying outlier behavior among sensors and subsystems is essential for discovering faults and facilitating diagnostics in large systems.
Event-enhanced Retrieval in Real-time Search
Furthermore, to strengthen the focus on critical event information in events, we include a decoder module after the document encoder, introduce a generative event triplet extraction scheme based on prompt-tuning, and correlate the events with query encoder optimization through comparative learning.
RAR-b: Reasoning as Retrieval Benchmark
Under the emerging Retrieval-augmented Generation (RAG) paradigm, we envision the need to evaluate next-level language understanding abilities of embedding models, and take a conscious look at the reasoning abilities stored in them.
