Semanlink - Favoris ; AI girls and guys ; NN 4 NLP AND Fine-tuning

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Favoris ; AI girls and guys ; NN 4 NLP AND Fine-tuning

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19 Documents

Jeremy Howard sur X : "FSDP/QDoRA with Llama3 : I believe that this combination is likely to create better task-specific models than anything else at any cost."

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2024-04-23 About

Sanjeev Arora sur X : "It's better to use just 5% of the instruction-tuning data (suitably selected) instead of the full dataset."

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2024-02-15 About

How many Labelled Examples do you need for a BERT-sized Model to Beat GPT-4 on Predictive Tasks? - Speaker Deck

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2023-10-27 About

fast.ai - Can LLMs learn from a single example?

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2023-10-21 About

Fine-Tuning a Linear Adapter for Any Embedding Model | LlamaIndex Blog | Sep, 2023

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2023-09-18 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

Easily Finetune Llama 2 for Your Text-to-SQL Applications | by Jerry Liu | Aug, 2023

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2023-08-18 About

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

Andrej Karpathy sur Twitter : "There's a chance that LoRA finetunes work so well that it dramatically alters the finetuning vs. retrieval + few-shot prompting power dynamic..."

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2023-04-21 About

karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.

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2023-02-02 About

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

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2022-09-26 About

[2209.11055] Efficient Few-Shot Learning Without Prompts

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2022-09-23 About

[2106.10199] BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

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2022-09-01 About

Nils Reimers sur Twitter : "GPL goes multi-lingual..."

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2022-06-01 About

Ramsri Goutham Golla sur Twitter : "Hi @Nils_Reimers For GPL you used "msmarco-distilbert-base-tas-b" model and ..."

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2022-04-27 About

Sentence Transformer Fine-Tuning (SetFit): Outperforming GPT-3 on few-shot Text-Classification while being 1600 times smaller | by Moshe Wasserblat (2021-12)

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2022-03-31 About

Nils Reimers sur Twitter : "Great post on SetFit"

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2022-03-31 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