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Favoris ; Education ; Information retrieval AND Arxiv Doc

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14 Documents

[2404.13950] SPLATE: Sparse Late Interaction Retrieval

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2024-04-23 About

[2401.18059] RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

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2024-02-03 About

[2304.09848] Evaluating Verifiability in Generative Search Engines

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2023-04-23 About

[2304.01982] Rethinking the Role of Token Retrieval in Multi-Vector Retrieval

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2023-04-05 About

[2212.01340] Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking

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2022-12-06 About

[2203.13088] Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction

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2022-03-30 About

[2004.12832] ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

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2022-01-12 About

[2007.00814] Relevance-guided Supervision for OpenQA with ColBERT

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2022-01-07 About

[2112.09118] Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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2021-12-21 About

[2112.01488] ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

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2021-12-05 About

[2109.08133] Phrase Retrieval Learns Passage Retrieval, Too

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2021-09-30 About

[2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering

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> a method to train an information retrieval module for downstream tasks, **without using pairs of queries and documents as annotations**.

Uses two models (standard pipeline for open-domain QA):

- the first one retrieves documents from a large source of knowledge (the retriever)
- the second one processes the support documents to solve the task (the reader).

> First the retriever selects support passages in a large knowledge
source. Then these passages are processed by the reader, along with the question, to generate an
answer

Inspired by knowledge distillation: the reader model is the teacher and the retriever is the student.

> More precisely, we use a sequence-to-sequence model as the reader, and use
the attention activations over the input documents as synthetic labels to train the retriever. 
> (**train the retriever by learning to approximate the attention score of the reader**)

Refers to:

- [REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)
- [Dehghani: Neural Ranking Models with Weak Supervision](doc:?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1704.08803)

2020-12-11 About

[2007.00077] Similarity Search for Efficient Active Learning and Search of Rare Concepts

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2020-07-02 About

[1911.00172] Generalization through Memorization: Nearest Neighbor Language Models

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2019-12-20 About