Fine-tuning (deep learning)

Last updated

In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2] A model may also be augmented with "adapters" that consist of far fewer parameters than the original model, and fine-tuned in a parameter-efficient way by tuning the weights of the adapters and leaving the rest of the model's weights frozen. [3]

Contents

For some architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the input layer) frozen, as they capture lower-level features, while later layers often discern high-level features that can be more related to the task that the model is trained on. [2] [4]

Models that are pre-trained on large, general corpora are usually fine-tuned by reusing their parameters as a starting point and adding a task-specific layer trained from scratch. [5] Fine-tuning the full model is also common and often yields better results, but is more computationally expensive. [6]

Fine-tuning is typically accomplished via supervised learning, but there are also techniques to fine-tune a model using weak supervision. [7] Fine-tuning can be combined with a reinforcement learning from human feedback-based objective to produce language models such as ChatGPT (a fine-tuned version of GPT models) and Sparrow. [8] [9]

Robustness

Fine-tuning can degrade a model's robustness to distribution shifts. [10] [11] One mitigation is to linearly interpolate a fine-tuned model's weights with the weights of the original model, which can greatly increase out-of-distribution performance while largely retaining the in-distribution performance of the fine-tuned model. [12]

Variants

Low-rank adaptation

Low-rank adaptation (LoRA) is an adapter-based technique for efficiently fine-tuning models. The basic idea is to design a low-rank matrix that is then added to the original matrix. [13] An adapter, in this context, is a collection of low-rank matrices which, when added to a base model, produces a fine-tuned model. It allows for performance that approaches full-model fine-tuning with lower space requirements. A language model with billions of parameters may be LoRA fine-tuned with only several millions of parameters.

LoRA-based fine-tuning has become popular in the Stable Diffusion community. [14] Support for LoRA was integrated into the diffusers library from Hugging Face. [15] Support for LoRA and similar techniques is also available for a wide range of other models through Hugging Face's parameter-efficient fine-tuning (PEFT) package. [16]

Representation fine-tuning

Representation fine-tuning (ReFT) is a technique developed by researchers at Stanford University aimed at fine-tuning large language models (LLMs) by modifying less than 1% of their representations. Unlike parameter-efficient fine-tuning (PEFT) methods, which mainly focus on updating weights, ReFT targets representations, suggesting that modifying representations might be a more effective strategy than updating weights. [17]

ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations and train interventions that manipulate a small fraction of model representations to steer model behaviors towards solving downstream tasks at inference time. One specific method within the ReFT family is low-rank linear subspace ReFT (LoReFT), which intervenes on hidden representations in the linear subspace spanned by a low-rank projection matrix. [17] LoReFT can be seen as the representation-based equivalent of low-rank adaptation (LoRA).

Applications

Natural language processing

Fine-tuning is common in natural language processing (NLP), especially in the domain of language modeling. Large language models like OpenAI's series of GPT foundation models can be fine-tuned on data for specific downstream NLP tasks (tasks that use a pre-trained model) to improve performance over the unmodified pre-trained model. [6]

Commercial models

Commercially-offered large language models can sometimes be fine-tuned if the provider offers a fine-tuning API. As of June 19, 2023, language model fine-tuning APIs are offered by OpenAI and Microsoft Azure's Azure OpenAI Service for a subset of their models, as well as by Google Cloud Platform for some of their PaLM models, and by others. [18] [19] [20]

See also

References

  1. ↑ Quinn, Joanne (2020). Dive into deep learning: tools for engagement. Thousand Oaks, California. p. 551. ISBN   978-1-5443-6137-6. Archived from the original on January 10, 2023. Retrieved January 10, 2023.{{cite book}}: CS1 maint: location missing publisher (link)
  2. 1 2 "CS231n Convolutional Neural Networks for Visual Recognition". cs231n.github.io. Retrieved 9 March 2023.
  3. ↑ Liu, Haokun; Tam, Derek; Muqeeth, Mohammed; Mohta, Jay; Huang, Tenghao; Bansal, Mohit; Raffel, Colin A (2022). Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K.; Oh, A. (eds.). Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (PDF). Advances in Neural Information Processing Systems. Vol. 35. Curran Associates, Inc. pp. 1950–1965.
  4. ↑ Zeiler, Matthew D; Fergus, Rob (2013). "Visualizing and Understanding Convolutional Networks". ECCV. arXiv: 1311.2901 .
  5. ↑ Dodge, Jesse; Ilharco, Gabriel; Schwartz, Roy; Farhadi, Ali; Hajishirzi, Hannaneh; Smith, Noah (2020). "Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping". arXiv: 2002.06305 .{{cite journal}}: Cite journal requires |journal= (help)
  6. 1 2 Dingliwal, Saket; Shenoy, Ashish; Bodapati, Sravan; Gandhe, Ankur; Gadde, Ravi Teja; Kirchhoff, Katrin (2021). "Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems". InterSpeech. arXiv: 2112.08718 .
  7. ↑ Yu, Yue; Zuo, Simiao; Jiang, Haoming; Ren, Wendi; Zhao, Tuo; Zhang, Chao (2020). "Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach". Association for Computational Linguistics. arXiv: 2010.07835 .
  8. ↑ "Introducing ChatGPT". openai.com. Retrieved 9 March 2023.
  9. ↑ Glaese, Amelia; McAleese, Nat; Trębacz, Maja; Aslanides, John; Firoiu, Vlad; Ewalds, Timo; Rauh, Maribeth; Weidinger, Laura; Chadwick, Martin; Thacker, Phoebe; Campbell-Gillingham, Lucy; Uesato, Jonathan; Huang, Po-Sen; Comanescu, Ramona; Yang, Fan; See, Abigail; Dathathri, Sumanth; Greig, Rory; Chen, Charlie; Fritz, Doug; Elias, Jaume Sanchez; Green, Richard; Mokrá, Soňa; Fernando, Nicholas; Wu, Boxi; Foley, Rachel; Young, Susannah; Gabriel, Iason; Isaac, William; Mellor, John; Hassabis, Demis; Kavukcuoglu, Koray; Hendricks, Lisa Anne; Irving, Geoffrey (2022). "Improving alignment of dialogue agents via targeted human judgements". DeepMind. arXiv: 2209.14375 .
  10. ↑ Radford, Alec; Kim, Jong Wook; Hallacy, Chris; Ramesh, Aditya; Goh, Gabriel; Agarwal, Sandhini; Sastry, Girish; Askell, Amanda; Mishkin, Pamela; Clark, Jack; Krueger, Gretchen; Sutskever, Ilya (2021). "Learning Transferable Visual Models From Natural Language Supervision". arXiv: 2103.00020 [cs.CV].
  11. ↑ Kumar, Ananya; Raghunathan, Aditi; Jones, Robbie; Ma, Tengyu; Liang, Percy (2022). "Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution". ICLR. arXiv: 2202.10054 .
  12. ↑ Wortsman, Mitchell; Ilharco, Gabriel; Kim, Jong Wook; Li, Mike; Kornblith, Simon; Roelofs, Rebecca; Gontijo-Lopes, Raphael; Hajishirzi, Hannaneh; Farhadi, Ali; Namkoong, Hongseok; Schmidt, Ludwig (2022). "Robust fine-tuning of zero-shot models". arXiv: 2109.01903 [cs.CV].
  13. ↑ Hu, Edward J.; Shen, Yelong; Wallis, Phillip; Allen-Zhu, Zeyuan; Li, Yuanzhi; Wang, Shean; Wang, Lu; Chen, Weizhu (2022-01-28). "LoRA: Low-Rank Adaptation of Large Language Models". ICLR. arXiv: 2106.09685 .
  14. ↑ Ryu, Simo (February 13, 2023). "Using Low-rank adaptation to quickly fine-tune diffusion models". GitHub. Retrieved June 19, 2023.
  15. ↑ Cuenca, Pedro; Paul, Sayak (January 26, 2023). "Using LoRA for Efficient Stable Diffusion Fine-Tuning". Hugging Face. Retrieved June 19, 2023.
  16. ↑ "Parameter-Efficient Fine-Tuning using 🤗 PEFT". huggingface.co. Retrieved 2023-06-20.
  17. 1 2 Wu, Zhengxuan; Arora, Aryaman; Wang, Zheng; Geiger, Atticus; Jurafsky, Dan; Manning, Christopher D.; Potts, Christopher (2024-04-07), ReFT: Representation Finetuning for Language Models, arXiv: 2404.03592
  18. ↑ "Fine-tuning". OpenAI. Retrieved 2023-06-19.
  19. ↑ "Learn how to customize a model for your application". Microsoft. Retrieved 2023-06-19.
  20. ↑ "Tune text foundation models" . Retrieved 2023-06-19.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.