This page lists fine-tuning methods such as Adaptors, LoRA, BitFit, NoisyTune, etc.

• Mosbach et al 2020 - On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines Gives a good baseline setting of hyperpameters for tuning BERT in section 6: fine-tune using ADAM with bias correction and a learning rate of 2e−5 for 20 epochs, with learning rate linearly increased for the first 10% of steps and linearly decayed to zero afterward.
• Ding et al 2022 - Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
• He et al 2022 - Towards a Unified View of Parameter-Efficient Transfer Learning
• Liu et al 2022 - Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
• Lv et al 2023 - Full Parameter Fine-tuning for Large Language Models with Limited Resources
• Lv et al 2023 - Low-memory Optimization with Adaptive Learning Rate
  • Lv et al 2023 - AdaLomo: Low-memory Optimization with Adaptive Learning Rate
• Han et al 2024 - Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
• 2024 - The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities Missing lots of stuff. Not really the ultimate guide.
• Szep et al 2024 - A Practical Guide to Fine-tuning Language Models with Limited Data
• Blog Posts, etc
  • Microsoft - Recommendations for LLM fine-tuning
  • Acorn - Fine-Tuning LLMs: Top 6 Methods, Challenges and Best Practices
  • OpenPipe - Fine-tuning Best Practices Chapter 2: Models

Figure from Mahabadi 2021.

See also Optimization - Instability of Fine-tuning.

• Lee et al 2019 - Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models
• Dodge et al 2020 - Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping The results can largely be mitigated by training for more epochs, see Mosbach 2020
• Zhou et al 2020 - IsoBN: Fine-Tuning BERT with Isotropic Batch Normalization
• Mosbach et al 2020 - On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines Advocates a simple baseline in section 6: fine-tune using ADAM with bias correction and a learning rate of 2e−5 for 20 epochs, with learning rate linearly increased for the first 10% of steps and linearly decayed to zero afterward.
• Zhang et al 2020 - Revisiting Few-sample BERT Fine-tuning
• Gradual Fine-Tuning: Xu et al 2021 - Gradual Fine-Tuning for Low-Resource Domain Adaptation
• Zhou & Srikumar 2021 - A Closer Look at How Fine-tuning Changes BERT
• EasyAdapt: Bai et al 2021 - Pre-train or Annotate? Domain Adaptation with a Constrained Budget Adapts Daumé III 2009 - Frustratingly Easy Domain Adaptation to Transformer era. Also considers the tradeoff between pretraining on in-domain data vs annotations on in-domain data under budget constraints.
• Xu et al 2021 - Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning Applies masking to only fine-tune a subset of the weights. Shows it outperforms regular fine-tuning.
• Wu et al 2022 - NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better Shows that adding some noise to the parameters (small perturbation) before fine-tuning can improve results.
• Malladi et al 2023 - Fine-Tuning Language Models with Just Forward Passes
• Qiu et al 2023 - Unlocking Emergent Modularity in Large Language Models
• Li et al 2024 - Gradient-Mask Tuning Elevates the Upper Limits of LLM Performance
• Removing the Causal Mask In Decoder-Only Models
  • 2024 - Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling Says “LLMs fall short of achieving state-of-the-art results in information extraction (IE) tasks, many of which are formulated as sequence labeling (SL)”
  • LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders Shows that Mistral was probably pre-trained using some bi-directional attention.

See also Memory Reduction Techniques.

• Adaptor Layers: Houlsby et al 2019 - Parameter-Efficient Transfer Learning for NLP
  • PyTorch code examples: PyTorch Adaptor Transformers Colab notebook tutorials Training an Adapter for a Transformer model
• P-tuning: Liu 2021 - GPT Understands, Too
• Mahabadi et al 2021 - Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
• Mahabadi et al 2021 - COMPACTER: Efficient Low-Rank Hypercomplex Adapter Layers
• LoRA: Hu et al 2021 - LoRA: Low-Rank Adaptation of Large Language Models
• Ben-Zaken et al 2021 - BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
• MAM-Adaptor: He et al 2022 - Towards a Unified View of Parameter-Efficient Transfer Learning
• Chen et al 2022 - Revisiting Parameter-Efficient Tuning: Are We Really There Yet?
• T-Few: Liu et al 2022 - Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
• QLoRA: Dettmers et al 2023 - QLORA: Efficient Finetuning of Quantized LLMs
• Malladi et al 2023 - Fine-Tuning Language Models with Just Forward Passes
• Hao et al 2024 - FLORA: Low-Rank Adapters Are Secretly Gradient Compressors
• Zhao et al 2024 - GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection Can also be used for pre-training
• Liu et al 2024 - DoRA: Weight-Decomposed Low-Rank Adaptation Says still often exists performance gap between PEFT and full fine-tuning (cited by He 2024 for this).
• He et al 2024 - Sparse Matrix in Large Language Model Fine-tuning

• Domain Adaptation
• Pretraining
• Prompting
• Prompt-Tuning, Soft-Prompting, etc
• NN Training