Semanlink - Favoris ; Technical girls and guys ; Search AND Representation learning

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Favoris ; Technical girls and guys ; Search AND Representation learning

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19 Documents

Nils Reimers sur X : "Embeddings can store only 1 aspect/topic per embedding well."

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2024-03-13 About

Jerry Liu sur X : "One major way to improve your RAG system is to fine-tune your embedding model"

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2023-08-25 About

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

@yoavgo sur Twitter : "searching by description can be very useful, but current embedding models will give you texts that are *similar* to the description, rather than texts that *adhere to it*..."

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2023-05-24 About

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

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2022-09-26 About

NAVER LABS Europe : "@Nils_Reimers of @huggingface on 'Unsupervised domain adaptation for neural search'"

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2022-03-09 About

[2112.09118] Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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2021-12-21 About

[2112.07577] GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

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An unsupervised domain adaptation technique for dense retrieval models

1. synthetic queries
are generated for each passage from the target corpus (using an existing pre-trained [T5](tag:text_to_text_transfer_transformer)
encoder-decoder)
2. the generated queries are used for mining negative
passages (retrieving the most similar
paragraphs using an existing dense retrieval
model == hard negatives!)
3. the query-passage pairs are labeled by a cross-encoder and used to train the domain-adapted
dense retriever (using method described in [Hofstätter et al.,
2020](doc:2021/12/2010_02666_improving_efficien))

[Nils Reimers sur Twitter](doc:2021/12/nils_reimers_sur_twitter_do_), [GitHub](https://github.com/UKPLab/gpl),  by the author of [TSDAE](doc:2021/09/2104_06979_tsdae_using_trans)

Claims to improve "Doc2Query" [Document Expansion by Query Prediction](doc:2022/01/1904_08375_document_expansion): ([src](https://twitter.com/KexinWang2049/status/1471435779415150598))

> - GPL: Uses doc2query to construct synthetic data and does knowledge distillation (i.e. training) on that data.
> - Doc2query: Generates queries to extend the documents and use BM25 on top of them w/o training.

2021-12-15 About

Nils Reimers sur Twitter : "New models for Neural Information Retrieval..."

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2021-04-17 About

TEXTUAL REPRESENTATION LEARNING DRIVEN BY KNOWLEDGE RESOURCES: APPLICATION TO INFORMATION RETRIEVAL

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2019-10-21 About

[1602.01137] A Dual Embedding Space Model for Document Ranking

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2019-07-17 About

A Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)

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2018-06-08 About

A Theoretical Approach to Semantic Coding and Hashing | Simons Institute for the Theory of Computing (2016)

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2018-05-26 About

Modèle Neuronal de Recherche d'Information Augmenté par une Ressource Sémantique (2017)

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2018-02-12 About

Semantics with Dense Vectors

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2017-11-11 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

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2017-06-09 About