Should I try multiple optimizers when fine-tuning a pre-trained Transformer for NLP tasks? Should I tune their hyperparameters? (2024.eacl-long)
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| Challenge: | NLP research has explored different neural model architectures and sizes, datasets, training objectives, and transfer-learning techniques. |
| Approach: | They propose to use a variant of Stochastic Gradient Descent (SGD) to select among numerous variants, often with minimal or no tuning of the optimizer’s hyperparameters. |
| Outcome: | Experiments with five GLUE datasets, two models and seven popular optimizers show that tuning just the learning rate is as good as tuning all the hyperparameters. |
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| Challenge: | State-of-the-art language models in NLP perform best when fine-tuned even on small datasets. |
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| Challenge: | Recent research has illuminated the possibility of selective parameter-efficient fine-tuning, which retains the inference speed of the original model and comes at no additional computational cost. |
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| Challenge: | In the recent years, pre-trained language models have achieved great success in the NLP community. |
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Know Where You’re Going: Meta-Learning for Parameter-Efficient Fine-Tuning (2023.findings-acl)
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| Challenge: | Existing studies on parameter-efficient fine-tuning methods require additional measures after pre-training and before fine-uning. |
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On Surgical Fine-tuning for Language Encoders (2023.findings-emnlp)
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Abhilasha Lodha, Gayatri Belapurkar, Saloni Chalkapurkar, Yuanming Tao, Reshmi Ghosh, Samyadeep Basu, Dmitrii Petrov, Soundararajan Srinivasan
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| Challenge: | In recent years, pretrained models revolutionized the paradigm of natural language understanding . but the final-layer output of the backbone, i.e. the input of the classification head, will change greatly during finetuning . |
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A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques (2024.acl-long)
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| Challenge: | Large language models are pre-trained on trillions of tokens and instruction-tuned or aligned to specific preferences. |
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