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Collections including paper arxiv:2103.10385

A collection of arXiv papers from Chip Huyen's AI Engineering organized by chapter and ordered by when each appears in the book.

Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning

Paper • 2211.04325 • Published Oct 26, 2022
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Paper • 1810.04805 • Published Oct 11, 2018 • 17
On the Opportunities and Risks of Foundation Models

Paper • 2108.07258 • Published Aug 16, 2021
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Paper • 2204.07705 • Published Apr 16, 2022 • 1

GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 9

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Paper • 2101.00190 • Published Jan 1, 2021 • 6
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 10
GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 9

Combining Modular Skills in Multitask Learning

Paper • 2202.13914 • Published Feb 28, 2022 • 4
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 10
Prefix-Tuning: Optimizing Continuous Prompts for Generation

Paper • 2101.00190 • Published Jan 1, 2021 • 6
GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 9

NLP Parameter Efficient Finetuning

Explore research optimizing NLP models for maximum performance with minimal computational resources.

Efficient Few-Shot Learning Without Prompts

Paper • 2209.11055 • Published Sep 22, 2022 • 3
Parameter-Efficient Transfer Learning for NLP

Paper • 1902.00751 • Published Feb 2, 2019 • 1
GPT Understands, Too

Paper • 2103.10385 • Published Mar 18, 2021 • 9
The Power of Scale for Parameter-Efficient Prompt Tuning

Paper • 2104.08691 • Published Apr 18, 2021 • 10

A collection of methods that have been implemented in the 🤗 PEFT library

Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Paper • 2303.10512 • Published Mar 18, 2023 • 2
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Paper • 2205.05638 • Published May 11, 2022 • 4
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

Paper • 2303.16199 • Published Mar 28, 2023 • 4
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning

Paper • 2108.06098 • Published Aug 13, 2021 • 2

LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Paper • 2310.08659 • Published Oct 12, 2023 • 27
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 44
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers

Paper • 2309.16119 • Published Sep 28, 2023 • 1
LoRA ensembles for large language model fine-tuning

Paper • 2310.00035 • Published Sep 29, 2023 • 2

Diversity of Thought Improves Reasoning Abilities of Large Language Models

Paper • 2310.07088 • Published Oct 11, 2023 • 5
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning

Paper • 2310.04474 • Published Oct 6, 2023 • 2
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques

Paper • 2310.08101 • Published Oct 12, 2023 • 2
Instance Needs More Care: Rewriting Prompts for Instances Yields Better Zero-Shot Performance

Paper • 2310.02107 • Published Oct 3, 2023 • 3

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