Semanlink - NLP: pretraining ; LM Fine-tuning AND Google

LlamaIndex 🦙 sur X : "Fine-tuning Embedding Models for RAG with LoRA'

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2024-04-23 About

UKP Lab sur X : "a lightweight solution for few-shot domain-specific sentence classification: AdaSent!..."

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2023-12-09 About

Google "We Have No Moat, And Neither Does OpenAI"

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> low-cost public involvement was enabled
by a vastly cheaper mechanism for fine tuning called low
rank adaptation ()[LoRA](tag:lora)

> **Part of what makes LoRA so effective is that ... it’s stackable.**
>
> By contrast, training giant models from scratch not only
throws away the pretraining, but also any iterative
improvements that have been made on top.

> LoRA updates are very cheap to produce (~$100) for the
most popular model sizes.

> Many of these projects are saving time by training on
small, highly curated datasets...
> These
datasets are built using synthetic methods (e.g. filtering
the best responses from an existing model) and
scavenging from other projects

> Directly Competing With Open Source
Is a Losing Proposition

> Paradoxically, the one clear winner in all of this is Meta.
Because the leaked model was theirs ([LLaMA](tag:llama)), they have
effectively garnered an entire planet's worth of free labor.
Since most open source innovation is happening on top of
their architecture, there is nothing stopping them from
directly incorporating it into their products.

2023-05-04 About

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

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2022-09-01 About

On Stability of Few-Sample Transformer Fine-Tuning | Kaggle

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2022-08-29 About

[2006.05987] Revisiting Few-sample BERT Fine-tuning

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2022-03-21 About

On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines (2021)

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2021-10-30 About

[1902.00751] Parameter-Efficient Transfer Learning for NLP

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2021-04-11 About