Information Retrieval
847 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 with no code
Retrieval Head Mechanistically Explains Long-Context Factuality
Despite the recent progress in long-context language models, it remains elusive how transformer-based models exhibit the capability to retrieve relevant information from arbitrary locations within the long context.
SPLATE: Sparse Late Interaction Retrieval
The late interaction paradigm introduced with ColBERT stands out in the neural Information Retrieval space, offering a compelling effectiveness-efficiency trade-off across many benchmarks.
Two-Step SPLADE: Simple, Efficient and Effective Approximation of SPLADE
Learned sparse models such as SPLADE have successfully shown how to incorporate the benefits of state-of-the-art neural information retrieval models into the classical inverted index data structure.
Evaluation of Machine Translation Based on Semantic Dependencies and Keywords
To achieve a comprehensive and in-depth evaluation of the semantic correctness of sentences, the experimental results show that the accuracy of the evaluation algorithm has been improved compared with similar methods, and it can more accurately measure the semantic correctness of machine translation.
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering
Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly those dominated by lengthy textual content like research journal articles.
Multi-Class Quantum Convolutional Neural Networks
The results show that with 4 classes, the performance is slightly lower compared to the classical CNN, while with a higher number of classes, the QCNN outperforms the classical neural network.
Towards Human-centered Proactive Conversational Agents
Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests.
iRAG: An Incremental Retrieval Augmented Generation System for Videos
Use of RAG for combined understanding of multimodal data such as text, images and videos is appealing but two critical limitations exist: one-time, upfront capture of all content in large multimodal data as text descriptions entails high processing times, and not all information in the rich multimodal data is typically in the text descriptions.
TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content
Our proposed approach achieves an IOU of 0. 96 and an OCR Accuracy of 78%, showcasing a remarkable improvement of approximately 25% in the OCR Accuracy compared to the previous Table Transformer approach.
Towards Compositionally Generalizable Semantic Parsing in Large Language Models: A Survey
Compositional generalization is the ability of a model to generalize to complex, previously unseen types of combinations of entities from just having seen the primitives.
