Semanlink - Language Model AND Information retrieval

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 A propos

Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

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2021-12-05 A propos

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

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2021-12-05 A propos

Unsupervised Training for Sentence Transformers | Pinecone

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2021-11-24 A propos

Multilingual Sentence Transformers | Pinecone

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2021-11-04 A propos

Next-Gen Sentence Embeddings with Multiple Negatives Ranking Loss | Pinecone

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2021-10-27 A propos

Isotropy in the Contextual Embedding Space: Clusters and Manifolds | OpenReview

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2021-10-26 A propos

Sentence Embeddings and Transformers | Pinecone

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2021-10-23 A propos

Building Scalable, Explainable, and Adaptive NLP Models with Retrieval | SAIL Blog

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2021-10-07 A propos

A Moderate Proposal for Radically Better AI-powered Web Search

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2021-07-10 A propos

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

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2021-04-17 A propos

[2007.12603] IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles

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2021-04-12 A propos

[1901.04085] Passage Re-ranking with BERT

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2021-03-26 A propos

Rodrigo Nogueira sur Twitter : "Slides of our WSDM 2021 tutorial "Pretrained Transformers for Text Ranking: BERT and Beyond"

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2021-03-09 A propos

Hugging Face sur Twitter : "Transformers release of the Retrieval-Augmented Generation model in collaboration with @facebookai!"

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2021-02-23 A propos

Retrieval Augmented Generation with Huggingface Transformers and Ray | Distributed Computing with Ray

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2021-02-11 A propos

[2002.08909] REALM: Retrieval-Augmented Language Model Pre-Training

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**Augment language model pre-training with a retriever module**, which
is trained using the masked language modeling objective.

> To capture knowledge in a more modular and interpretable way, we augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. **For the first time, we show how to pre-train such a knowledge retriever in an unsupervised manner**, using masked language modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents

Hum, #TODO: parallel to be drawn with techniques in [KG-augmented Language Models](tag:knowledge_graph_augmented_language_models) which focus "on the problem of capturing declarative knowledge in the learned parameters of a language model."

[Google AI Blog Post](doc:2020/08/google_ai_blog_realm_integrat)

[Summary](https://joeddav.github.io/blog/2020/03/03/REALM.html) for the [Hugging Face awesome-papers reading group](doc:2021/03/huggingface_awesome_papers_pap)

2020-12-12 A propos

[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 A propos

Google AI Blog: REALM: Integrating Retrieval into Language Representation Models

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