Semanlink - NLP Teams ; NLP girls and guys AND Information retrieval: techniques

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AI: books & journals - Arxiv Doc - Benjamin Clavié - Chris Manning - Dan Jurafsky - Discuté avec Raphaël - Domain adaptation - Embeddings in Information Retrieval - EMNLP 2021 - Extreme Multi-label Classification - Gautier Izacard - Generalization - GitHub project - Good - Hard negative mining - Information retrieval - Information retrieval: techniques - Knowledge-augmented Language Models - Knowledge distillation - Knowledge Extraction - Knowledge-Intensive NLP Tasks - LM + Search - Manik Varma - Michael Glass - Neural Search - Nils Reimers - NLP@ENS - NLP@Facebook - NLP@IBM - NLP@Microsoft - NLP@Stanford - NLP based IR - Omar Khattab - Prompting/In-context learning - Question Answering - RAGatouille - Raphaël Sourty - Retriever-Reader - SlideShare - Slot filling - Word embeddings - Zero shot -

NLP Teams ; NLP girls and guys AND Information retrieval: techniques

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14 Documents

[2404.13950] SPLATE: Sparse Late Interaction Retrieval

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2024-04-23 About

bclavie/RAGatouille

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2024-01-26 About

raphaelsty/neural-cherche: Neural Search

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2023-11-17 About

stanfordnlp/dspy: 𝗗𝗦𝗣: Demonstrate-Search-Predict. A framework for composing retrieval and language models for knowledge-intensive NLP.

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2023-02-18 About

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

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2022-09-26 About

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

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2022-01-19 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

SLEEC: Sparse Local Embeddings for Extreme Multi-label Classification (2015)

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2020-08-11 About

[1911.00172] Generalization through Memorization: Nearest Neighbor Language Models

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2019-12-20 About

[1602.01137] A Dual Embedding Space Model for Document Ranking

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2019-07-17 About

Distributed Word Representations for Information Retrieval

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2017-10-01 About

Vectorland: Brief Notes from Using Text Embeddings for Search

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2017-09-18 About

Using Text Embeddings for Information Retrieval

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2017-09-18 About

Introduction to Information Retrieval, Cambridge University Press (2008) Manning, Raghavan, and Schütze

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2012-04-01 About