Semanlink - Attention mechanism ; Question Answering AND Information retrieval

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Attention mechanism ; Question Answering AND Information retrieval

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18 Documents

raphaelsrty sur X : "My personal Knowledge Base made to the front page of HackerNews today..."

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2024-03-23 About

Notebook by OpenAI which demos a Q&A workflow using ChatGPT API· openai/openai-cookbook

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2023-04-15 About

openai/chatgpt-retrieval-plugin: The ChatGPT Retrieval Plugin

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2023-04-13 About

Domain Adaptation with Generative Pseudo-Labeling (GPL) | Pinecone

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2023-04-09 About

Sinequa Brings the Power of ChatGPT to the Enterprise With Neural Search

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2023-04-07 About

Jo Kristian Bergum sur Twitter : "Do GTP embeddings work for our domain?"

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2023-04-07 About

Jo Kristian Bergum sur Twitter : "The movement around connecting LLMs with your data via retrieval augmentation seems to lack a focus on retrieval quality."

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2023-04-07 About

[2009.13013] SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval

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2023-04-06 About

Release v1.15.0 · deepset-ai/haystack

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2023-03-30 About

whitead/paper-qa: LLM Chain for answering questions from documents with citations

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2023-03-29 About

raphaelsty.github.io/knowledge demo

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2023-03-15 About

Chatbots Gone Wild, Surveillance Takes Hold, Rules for Military AI, Robot Training Streamlined

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2023-02-23 About

‘I want to destroy whatever I want’: Bing’s AI chatbot unsettles US reporter | Artificial intelligence (AI) | The Guardian

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Bing+ChatGPT

2023-02-18 About

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perplexity.ai

2023-01-20 About

Akari Asai sur Twitter : "Can we solely rely on LLMs’ memories (eg replace search w ChatGPT)? Probably not... Our analysis shows how retrieval is complementary to LLMs’ parametric knowledge..."

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2022-12-21 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

[2112.07577] 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