Semanlink - Favoris ; Machine learning: techniques ; NLP: pretraining ; NLP and Search AND Question Answering

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Favoris ; Machine learning: techniques ; NLP: pretraining ; NLP and Search AND Question Answering

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20 Documents

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

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2024-03-23 About

Customizing Agent to Chat with Your Documents | Haystack

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2023-07-25 About

PromptHub

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2023-07-07 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

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

deepset sur Twitter : " Generative models have taken the world of NLP by storm. But LLMs do not know about your personal data. This makes personal assistants, enterprise knowledge management and many other applications challenging. Retrieval augmented pipelines are the answer"

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2023-02-02 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

[2207.06300] Re2G: Retrieve, Rerank, Generate

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2022-07-14 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