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https://github.com/mmarius/awesome-finetuning
A curated list of resources on fine-tuning language models.
https://github.com/mmarius/awesome-finetuning
List: awesome-finetuning
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A curated list of resources on fine-tuning language models.
- Host: GitHub
- URL: https://github.com/mmarius/awesome-finetuning
- Owner: mmarius
- License: mit
- Created: 2021-11-17T09:53:16.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-08-18T15:12:09.000Z (about 2 years ago)
- Last Synced: 2024-04-11T22:06:38.578Z (7 months ago)
- Size: 60.5 KB
- Stars: 16
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# Awesome Fine-tuning [](https://github.com/sindresorhus/awesome)
A curated list of resources on fine-tuning language models, inspired by [awesome-implicit-representations](https://github.com/vsitzmann/awesome-implicit-representations).
## Disclaimer
This list does __not aim to be exhaustive__. Feel free to open a pull request in order to suggest papers that should be added to the list.
Disclosure. I'm an author of the following papers:
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://openreview.net/forum?id=nzpLWnVAyah)
- [On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers](https://aclanthology.org/2020.findings-emnlp.227/)
## Table of contents
- [Papers](#papers)
- [Fine-tuning before transformers](#fine-tuning-before-transformers)
- [Fine-tuning transformers](#fine-tuning-transformers)
- [Fine-tuning stability](#fine-tuning-stability)
- [Intermediate task fine-tuning](#intermediate-task-fine-tuning)
- [Parameter-efficient fine-tuning](#parameter-efficient-fine-tuning)
- [Prompt-based fine-tuning](#prompt-based-fine-tuning)
- [Evaluating few-shot fine-tuning](#evaluating-few-shot-fine-tuning)
- [Fine-tuning analysis](#fine-tuning-analysis)
- [Theoretical work](#theoretical-work)
- [Surveys](#surveys)
- [Misc.](#misc)
# Papers
## Fine-tuning before transformers
- [Semi-supervised Sequence Learning](https://arxiv.org/abs/1511.01432) Dai & Le (2015) 
- [How Transferable are Neural Networks in NLP Applications?](https://aclanthology.org/D16-1046/) Mou et al. (2016) 
- [Improving Neural Machine Translation Models with Monolingual Data](https://aclanthology.org/P16-1009/) Sennrich et al. (2016) 
- [Question Answering through Transfer Learning from Large Fine-grained Supervision Data](https://aclanthology.org/P17-2081/) Min et al. (2017) 
- [Universal Language Model Fine-tuning for Text Classification](https://aclanthology.org/P18-1031/) Howard & Ruder (2018) 
- [An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models](https://aclanthology.org/N19-1213/) Chronopoulou et al. (2019) 
- ...
## Fine-tuning transformers
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/) Devlin et al. (2019) 
- [Better Fine-Tuning by Reducing Representational Collapse](https://arxiv.org/abs/2008.03156) Aghajanyan et al. (2020) 
- [FreeLB: Enhanced Adversarial Training for Natural Language Understanding](https://openreview.net/forum?id=BygzbyHFvB) Zhu et al. (2020) 
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://aclanthology.org/2020.acl-main.197/) Jiang et al. (2020) 
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://openreview.net/forum?id=cu7IUiOhujH) Gunel et al. (2021) 
- ...
### Intermediate task fine-tuning
- [Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks](https://arxiv.org/abs/1811.01088) Phang et al. (2018) 
- [Transfer Fine-Tuning: A BERT Case Study](https://aclanthology.org/D19-1542) Arase & Tsujii (2019) 
- [Learning and Evaluating General Linguistic Intelligence](https://arxiv.org/abs/1901.11373) Yogatama et al. (2019) 
- [Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://aclanthology.org/2020.acl-main.467/) Pruksachatkun et al. (2020) 
- [English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too](https://arxiv.org/abs/2005.13013) Phang et al. (2020) 
- [What to Pre-Train on? Efficient Intermediate Task Selection](https://aclanthology.org/2021.emnlp-main.827/) Poth et al. (2021) 
- [Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation](https://aclanthology.org/2021.eacl-main.270/) Glavaš & Vulić (2021) 
- [Muppet: Massive Multi-task Representations with Pre-Finetuning]() Aghajanyan et al. (2021) 
- ...
#### Intermediate (masked) language modeling
- [Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling](https://aclanthology.org/D19-1433/) Han & Eisenstein (2019) 
- [Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks](https://aclanthology.org/2020.acl-main.740/) Gururangan et al. (2020) 
- [Mining Knowledge for Natural Language Inference from Wikipedia Categories](https://aclanthology.org/2020.findings-emnlp.313/) Chen et al. (2020) 
- [Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank](https://aclanthology.org/2020.findings-emnlp.118/) Chau et al. (2020) 
- [Train No Evil: Selective Masking for Task-Guided Pre-Training](https://aclanthology.org/2020.emnlp-main.566/) Gu et al. (2020) 
- ...
##### Injecting "skills"
- [Injecting Numerical Reasoning Skills into Language Models](https://aclanthology.org/2020.acl-main.89/) Geva et al. (2020) 
- [Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers](https://aclanthology.org/2020.deelio-1.5/) Lauscher et al. (2020) 
- [Analyzing Commonsense Emergence in Few-shot Knowledge Models](https://openreview.net/forum?id=StHCELh9PVE) Da et al. (2021) 
- ...
### Parameter-efficient fine-tuning
- [Parameter-Efficient Transfer Learning for NLP](https://proceedings.mlr.press/v97/houlsby19a) Houlsby et al. (2019) 
- [BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning](http://proceedings.mlr.press/v97/stickland19a.html) Stickland & Murray (2019) 
- [Simple, Scalable Adaptation for Neural Machine Translation](https://aclanthology.org/D19-1165/) Bapna & Firat (2019) 
- [Masking as an Efficient Alternative to Finetuning for Pretrained Language Models](https://aclanthology.org/2020.emnlp-main.174/) Zhao et al. (2020) 
- [Movement Pruning: Adaptive Sparsity by Fine-Tuning](https://arxiv.org/abs/2005.07683) Sanh et al. (2020) 
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://aclanthology.org/2021.eacl-main.39/) Pfeiffer et al. (2021) 
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://aclanthology.org/2020.emnlp-main.617/) Pfeiffer et al. (2020) 
- [AdapterDrop: On the Efficiency of Adapters in Transformers](https://aclanthology.org/2021.emnlp-main.626/) Rücklé et al. (2021) 
- [Parameter-efficient transfer learning with diff pruning](https://aclanthology.org/2021.acl-long.378/) Guo et al. (2021) 
- [Compacter: Efficient Low-Rank Hypercomplex Adapter Layers](https://arxiv.org/abs/2106.04647) Mahabadi et al. (2021) 
- [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) Hu et al. (2021) 
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://arxiv.org/abs/2106.10199) Zaken et al. (2022) 
- [Training Neural Networks with Fixed Sparse Masks](https://proceedings.neurips.cc/paper/2021/hash/cb2653f548f8709598e8b5156738cc51-Abstract.html) Sung et al. (2021) 
- [Towards a Unified View of Parameter-Efficient Transfer Learning](https://openreview.net/forum?id=0RDcd5Axok) He et al. (2021) 
- [Composable Sparse Fine-Tuning for Cross-Lingual Transfer](https://arxiv.org/abs/2110.07560) Ansell et al. (2022) 
- [Revisiting Parameter-Efficient Tuning: Are We Really There Yet?](https://arxiv.org/abs/2202.07962) Chen et al. (2022) 
- [Prompt-free and Efficient Few-shot Learning with Language Models](https://aclanthology.org/2022.acl-long.254/) Mahabadi et al. (2022) 
- [Adaptable Adapters](https://aclanthology.org/2022.naacl-main.274/) Moosavi et al. (2022) 
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://arxiv.org/abs/2205.05638) Liu et al. (2022) 
- ...
Some continuous prompt-based methods can also be seen as parameter-efficient fine-tuning methods. For a list of papers see [below](#continuous-prompts).
### Prompt-based fine-tuning
#### Discrete prompts
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://aclanthology.org/2021.eacl-main.20/) Schick & Schütze (2021a) 
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://aclanthology.org/2021.naacl-main.185/) Schick & Schütze (2021b) 
- [Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification](https://aclanthology.org/2020.coling-main.488/) Schick et al. (2020) 
- [Few-Shot Text Generation with Natural Language Instructions](https://aclanthology.org/2021.emnlp-main.32/) Schick & Schütze (2021c) 
- [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295/) Gao et al. (2021) 
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) Shin et al. (2020) 
- [How Many Data Points is a Prompt Worth?](https://aclanthology.org/2021.naacl-main.208/) Le Scao & Rush (2021) 
- [Improving and Simplifying Pattern Exploiting Training](https://aclanthology.org/2021.emnlp-main.407/) Tam et al. (2021) 
- [Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections](https://aclanthology.org/2021.findings-emnlp.244/) Zhong et al. (2021) 
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) Zhao et al. (2021) 
- [PTR: Prompt Tuning with Rules for Text Classification](https://arxiv.org/abs/2105.11259) Han et al. (2021) 
- [Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models](https://arxiv.org/abs/2106.13353) Logan IV et al. (2021) 
- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://arxiv.org/abs/2108.02035) Hu et al. (2021) 
- [Prompt-Learning for Fine-Grained Entity Typing](https://arxiv.org/abs/2108.10604) Ding et al. (2021) 
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://arxiv.org/abs/2109.01247) Webson & Pavlick (2022) 
- [Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning](https://aclanthology.org/2021.emnlp-main.713/) Utama et al. (2021) 
- [Prototypical Verbalizer for Prompt-based Few-shot Tuning](https://aclanthology.org/2022.acl-long.483/) Cui et al. (2022) 
- ...
#### Multi-task fine-tuning using discrete prompts
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://arxiv.org/abs/2104.08773) Mishra et al. (2021) 
- [Discrete and Soft Prompting for Multilingual Models](https://aclanthology.org/2021.emnlp-main.672/) Zhao & Schütze (2021) 
- [Finetuned Language Models Are Zero-Shot Learners](https://openreview.net/forum?id=gEZrGCozdqR) Wei et al. (2021) 
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://openreview.net/forum?id=9Vrb9D0WI4) Sanh et al. (2021) 
- [Prompt Consistency for Zero-Shot Task Generalization](https://arxiv.org/abs/2205.00049) Zhou et al. (2022) 
- [Few-shot Adaptation Works with UnpredicTable Data](https://arxiv.org/abs/2208.01009) Chan et al. (2022) 
- [Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks](https://arxiv.org/abs/2204.07705) Wang et al. (2022) 
- ...
#### Continuous prompts
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) Li & Liang (2021) 
- [WARP: Word-level Adversarial ReProgramming](https://aclanthology.org/2021.acl-long.381/) Hambardzumyan et al. (2021) 
- [Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://aclanthology.org/2021.naacl-main.410/) Qin & Eisner (2021) 
- [Factual Probing Is [MASK]: Learning vs. Learning to Recall](https://aclanthology.org/2021.naacl-main.398/) Zhong et al. (2021) 
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://aclanthology.org/2021.emnlp-main.243/) Lester et al. (2021) 
- [Multimodal Few-Shot Learning with Frozen Language Models](https://papers.nips.cc/paper/2021/hash/01b7575c38dac42f3cfb7d500438b875-Abstract.html) Tsimpoukelli et al. (2021) 
- [Noisy Channel Language Model Prompting for Few-Shot Text Classification](https://arxiv.org/abs/2108.04106) Min et al. (2021) 
- [Continuous Entailment Patterns for Lexical Inference in Context](https://aclanthology.org/2021.emnlp-main.556/) Schmitt & Schütze (2021) 
- [Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners](https://openreview.net/forum?id=ek9a0qIafW) Zhang et al. (2022) 
- [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://aclanthology.org/2022.acl-long.346/) Vu et al. (2022) 
- [P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks](https://aclanthology.org/2022.acl-short.8/) Liu et al. (2022) 
- ...
### Evaluating few-shot fine-tuning
- [True Few-Shot Learning with Language Models](https://proceedings.neurips.cc/paper/2021/hash/5c04925674920eb58467fb52ce4ef728-Abstract.html) Perez et al. (2021) 
- [FLEX: Unifying Evaluation for Few-Shot NLP](https://papers.nips.cc/paper/2021/hash/8493eeaccb772c0878f99d60a0bd2bb3-Abstract.html) Bragg et al. (2021) 
- [FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding](https://arxiv.org/abs/2109.12742) Zheng et al. (2022) 
- [True Few-Shot Learning with Prompts—A Real-World Perspective](https://aclanthology.org/2022.tacl-1.41/) Schick & Schütze (2022) 
- ...
### Fine-tuning analysis
- [Visualizing and Understanding the Effectiveness of BERT](https://aclanthology.org/D19-1424/) Hao et al. (2019) 
- [oLMpics-On What Language Model Pre-training Captures](https://aclanthology.org/2020.tacl-1.48/) Talmor et al. (2020) 
- [Pretrained Transformers Improve Out-of-Distribution Robustness](https://aclanthology.org/2020.acl-main.244/) Hendrycks et al. (2020) 
- [What Happens To BERT Embeddings During Fine-tuning?](https://aclanthology.org/2020.blackboxnlp-1.4/) Merchant et al. (2020) 
- [Investigating Learning Dynamics of BERT Fine-Tuning](https://aclanthology.org/2020.aacl-main.11/) Hao et al. (2020) 
- [Investigating Transferability in Pretrained Language Models](https://aclanthology.org/2020.findings-emnlp.125/) Tamkin et al. (2020) 
- [Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://aclanthology.org/2021.acl-long.568/) Aghajanyan et al. (2021) 
- [Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers](https://aclanthology.org/2021.blackboxnlp-1.42/) Phang et al. (2021) 
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://openreview.net/forum?id=UYneFzXSJWh) Kumar et al. (2022) 
- [A Closer Look at How Fine-tuning Changes BERT]() Zhou & Srikumar (2022) 
- [Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://aclanthology.org/2021.acl-long.568/) Aghajanyan et al. (2021) 
- [When Do You Need Billions of Words of Pretraining Data?](https://aclanthology.org/2021.acl-long.90/) Zhang et al. (2021) 
- [On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation](https://aclanthology.org/2021.acl-long.172/) He et al. (2021) 
- [Pretrained Transformers as Universal Computation Engines](https://arxiv.org/abs/2103.05247) Lu et al. (2021) 
- [Predicting Inductive Biases of Pre-Trained Models](https://openreview.net/forum?id=mNtmhaDkAr) Lovering et al. (2021) 
- [Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers](https://aclanthology.org/2021.blackboxnlp-1.42/) Phang et al. (2021) 
- ...
#### Fine-tuning stability
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://arxiv.org/abs/2002.06305) Dodge et al. (2020) 
- [Revisiting Few-sample BERT Fine-tuning]() Zhang et al. (2021) 
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://openreview.net/forum?id=nzpLWnVAyah) Mosbach et al. (2021) 
- ...
#### Fine-tuning and probing
- [What Happens To BERT Embeddings During Fine-tuning?](https://aclanthology.org/2020.blackboxnlp-1.4/) Merchant et al. (2020) 
- [On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers](https://aclanthology.org/2020.findings-emnlp.227/) Mosbach et al. (2020)  
- [On the Importance of Data Size in Probing Fine-tuned Models](https://aclanthology.org/2022.findings-acl.20/) Mehrafarin et al. (2022) 
- ...
#### Fine-tuning and generalization
- [BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance](https://aclanthology.org/2020.blackboxnlp-1.21/) McCoy et al. (2020) 
- [Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics](https://aclanthology.org/2021.insights-1.18/) Bhagava et al. (2021) 
- [Linear Connectivity Reveals Generalization Strategies]() Juneja et al. (2022) 
- ...
##### Fine-tuning and spurious features
- [An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models](https://aclanthology.org/2020.tacl-1.40/) Tu et al. (2020) 
- [Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually)](https://aclanthology.org/2020.emnlp-main.16/) Warstadt et al. (2020) 
- [Predicting Inductive Biases of Pre-Trained Models](https://openreview.net/forum?id=mNtmhaDkAr) Lovering et al. (2021) 
- ...
## Theoretical work
- [A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks](https://openreview.net/forum?id=vVjIW3sEc1s) Saunshi et al. (2021) 
- [Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning](https://arxiv.org/abs/2106.09226) Wei et al. (2021) 
- ...
## Surveys
- [Recent Advances in Language Model Fine-tuning]() Ruder (2021) 
- [On the Opportunities and Risks of Foundation Models](https://arxiv.org/abs/2108.07258) *(Adaptation chapter)* Bommasani et al. (2021) 
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) Liu et al. (2021) 
- [Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models](https://arxiv.org/abs/2203.06904) Ding et al. (2022) 
- ...
## Misc.
- [What is being transferred in transfer learning?](https://arxiv.org/abs/2008.11687) Neyshabur et al. (2020) 
- [Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge](https://arxiv.org/abs/2006.06609) Talmor et al. (2020) 
- [Exploring and Predicting Transferability across NLP Tasks](https://aclanthology.org/2020.emnlp-main.635/) Vu et al. (2020) 
- ...