Parameter efficient fine tuning is increasingly important in NLP and genAI. Let's talk about it.
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[1 ?] RNNs were the SOTA for sequence tasks: https://arxiv.org/pdf/1409.0473
[2 ?] Then transformers came on the scene: https://arxiv.org/pdf/1706.03762
[3 ?] Pretraining and Finetuning architectures like BERT came along: https://arxiv.org/pdf/1810.04805
[4 ?] But LLMs are huge: https://informationisbeautiful.net/visualizations/the-rise-of-generative-ai-large-language-models-llms-like-chatgpt/
[5 ?] Few shot learning by GPT-3 tries to address the issue: https://arxiv.org/pdf/2005.14165
[6 ?] Parameter Efficient Transfer Learning reduces the trainable parameters via additive adapters (the first PEFT technique): https://arxiv.org/pdf/1902.00751
[7 ?] Since 2019, there have been many PEFT techniques introduced: https://arxiv.org/pdf/2312.12148
[8 ?] Other notable techniques include prefix-tuning: https://arxiv.org/pdf/2101.00190
[9 ?] And LoRA: https://arxiv.org/pdf/2106.09685
[10 ?] And a quantized version of LoRA called QLoRA: https://arxiv.org/pdf/2305.14314
[11 ?] We see these adapters in use in LLMs today like Llama: https://arxiv.org/pdf/2303.16199
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CHAPTERS
0:00 Introduction
1:00 Pass 1: What & Why PEFT
6:27 Quiz 1
7:26 Pass 2: Details
16:20 Quiz 2
17:11 Pass 3: Performance Evaluation
20:49 Quiz 3
21:43 Summary
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