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Found 13 papers, 6 papers with code
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.
A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE
We present a comparative study between cross-encoder and LLMs rerankers in the context of re-ranking effective SPLADE retrievers.
SPLADE-v3: New baselines for SPLADE
A companion to the release of the latest version of the SPLADE library.
Benchmarking Middle-Trained Language Models for Neural Search
We compare both dense and sparse approaches under various finetuning protocols and middle training on different collections (MS MARCO, Wikipedia or Tripclick).
Query Performance Prediction for Neural IR: Are We There Yet?
On top of that, in lexical-oriented scenarios, QPPs fail to predict performance for neural IR systems on those queries where they differ from traditional approaches the most.
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval
Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history.
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective
Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture.
MS-Shift: An Analysis of MS MARCO Distribution Shifts on Neural Retrieval
To this end, we build three query-based distribution shifts within MS MARCO (query-semantic, query-intent, query-length), which are used to evaluate the three main families of neural retrievers based on BERT: sparse, dense, and late-interaction -- as well as a monoBERT re-ranker.
Composite Code Sparse Autoencoders for first stage retrieval
Second, CCSA can be used as a binary quantization method and we propose to combine it with the recent graph based ANN techniques.
Match Your Words! A Study of Lexical Matching in Neural Information Retrieval
Neural Information Retrieval models hold the promise to replace lexical matching models, e. g. BM25, in modern search engines.
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
Meanwhile, there has been a growing interest in learning \emph{sparse} representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes.
Ranked #5 on
Zero-shot Text Search
on BEIR
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
In neural Information Retrieval, ongoing research is directed towards improving the first retriever in ranking pipelines.
A White Box Analysis of ColBERT
Transformer-based models are nowadays state-of-the-art in ad-hoc Information Retrieval, but their behavior is far from being understood.
