Efficient and Robust Fine-Tuning of Pre-Trained Language Models by Transferring Training Dynamics

Core Concepts

Training dynamics are highly transferable across model sizes and pre-training methods, enabling efficient and robust fine-tuning of pre-trained language models.

Abstract

This paper proposes a novel fine-tuning approach called Fine-Tuning by transFerring Training dynamics (FTFT) that improves the robustness and efficiency of fine-tuning pre-trained language models (PLMs). The key insights are: Training dynamics (i.e., instance prediction probabilities during fine-tuning) are highly transferable across different model sizes and pre-training methods. This allows using more efficient reference models to construct data maps (DMs) for fine-tuning larger main models. Fine-tuning main models using training instances selected by DMs achieves consistently higher training speed than conventional fine-tuning based on empirical risk minimization (ERM). Building on these observations, FTFT uses efficient reference models and aggressive early stopping to achieve robustness improvements over ERM while lowering the training cost by up to ~50%. The authors conduct experiments on Natural Language Inference (NLI) and Hate Speech Detection (HSD) tasks. They show that: Training dynamics are transferable across different model sizes and pre-training methods, enabling the use of efficient reference models. Fine-tuning with data selected by DMs leads to faster training speed compared to ERM. FTFT achieves better robustness on out-of-distribution (OOD) data than ERM, while reducing the training cost by up to ~50%.

Stats

Fine-tuning large PLMs is computationally expensive and they lack robustness against out-of-distribution (OOD) inputs. Dataset cartography, a dual-model approach, can improve model robustness but is computationally expensive. Training dynamics are highly transferable across different model sizes and pre-training methods. Fine-tuning with data selected by data maps (DMs) achieves consistently higher training speed than ERM.

Quotes

"Training dynamics are highly transferable across different model sizes and pretraining methods, enabling the exploitation of efficient reference models." "Fine-tuning using training instances selected by DMs enjoys consistently higher training efficiency than conventional fine-tuning." "FTFT achieves consistent robustness improvement over ERM, indicated by its strong performance on most OOD datasets, while lowering the training cost by up to ~50%."

Key Insights Distilled From

FTFT

by Yupei Du,Alb... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2310.06588.pdf

Deeper Inquiries

How can the transferability of training dynamics be further leveraged to improve the efficiency and robustness of fine-tuning in other NLP tasks beyond classification, such as generation and self-supervised learning

The transferability of training dynamics can be further leveraged to improve the efficiency and robustness of fine-tuning in other NLP tasks beyond classification by applying similar principles to tasks such as generation and self-supervised learning. For generation tasks, the training dynamics can be used to identify important training instances that contribute to the quality of generated outputs. By selecting and focusing on these instances during fine-tuning, models can learn to generate more coherent and accurate text. In self-supervised learning, training dynamics can help in identifying instances that are crucial for learning meaningful representations of the input data. By fine-tuning based on these selected instances, models can improve their ability to capture the underlying structure and semantics of the data, leading to better performance in self-supervised tasks.

What are the theoretical foundations underlying the transferability of training dynamics and the effectiveness of data selection based on training dynamics

The theoretical foundations underlying the transferability of training dynamics and the effectiveness of data selection based on training dynamics lie in the concept of data importance and model generalization. Training dynamics capture how models learn from different training instances over the course of training. The transferability of training dynamics across different model sizes and pretraining methods suggests that certain patterns in the learning process are consistent and can be leveraged to improve model performance. Effective data selection based on training dynamics relies on the ability of reference models to identify ambiguous, hard-to-learn, and easy instances. By selecting instances that are challenging for the model, fine-tuning can focus on improving performance on these critical data points, leading to better generalization and robustness.

Can the insights from this work be extended to develop more rigorous protocols for choosing efficient yet effective reference models without training the main model, which is computationally expensive

The insights from this work can be extended to develop more rigorous protocols for choosing efficient yet effective reference models without training the main model, which is computationally expensive. By further investigating the characteristics of effective reference models, such as their ability to identify important training instances accurately, researchers can develop guidelines for selecting reference models that are both computationally efficient and effective in improving model performance. These protocols can involve evaluating the performance of reference models on specific subsets of the data, analyzing their training dynamics, and determining their impact on the robustness and efficiency of the fine-tuning process. By establishing clear criteria for selecting reference models, researchers can streamline the process of fine-tuning and enhance the overall performance of NLP models.