Semanlink - Information retrieval ; NLP tasks / problems AND Retriever-Reader

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Information retrieval ; NLP tasks / problems AND Retriever-Reader

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BLINK

15 Documents

Ravi Theja sur X : "𝐀𝐜𝐭𝐢𝐯𝐞𝐑𝐀𝐆: 𝐑𝐞𝐯𝐞𝐚𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐫𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐨𝐟 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐯𝐢𝐚 𝐀𝐜𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠..."

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2024-02-25 About

LlamaIndex 🦙 sur X : "Use RAG to build advanced text-to-SQL..."

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2024-01-24 About

L. Dietz @deeds@mastodon.social @dietz@idf.social sur X : "How to evaluate RAG / RACAS (Retrieval-Augmented-Clustering-Augmented-Summarization) systems for Article Generation..."

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2023-10-20 About

Jerry Liu sur X : "This might be the first time ChatGPT (+@jxnlco) helped us come up with a better retrieval algorithm for RAG..."

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2023-08-28 About

Yi Ding -- prod/acc sur X : "First is best for retrieval when using ChatGPT models."

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2023-08-23 About

LlamaIndex: Harnessing the Power of Text2SQL and RAG to Analyze Product Reviews | by Ravi Theja | LlamaIndex Blog | Aug, 2023 | Medium

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2023-08-13 About

Jerry Liu sur Twitter : "Using cross-encoding as a reranking step can dramatically speed up LLM inference time AND improve accuracy!"

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2023-07-20 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

Stanford NLP Group sur Twitter : "YONO: You Only Need One Model for Open-domain Question Answering..."

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2022-12-05 About

[2207.06300] Re2G: Retrieve, Rerank, Generate

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2022-07-14 About

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

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2022-01-19 About

[2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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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

[1911.03814] Scalable Zero-shot Entity Linking with Dense Entity Retrieval

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2020-05-02 About

Sebastian Riedel sur Twitter : Happy to introduce BLINK, the @facebookai open-source entity linker!...

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2020-05-02 About