Data-Efficient Fine-Tuning of Pre-Trained Language Models via Unsupervised Core-Set Selection

Core Concepts

A data-efficient fine-tuning framework, DEFT-UCS, leverages unsupervised core-set selection to identify a smaller, representative dataset that reduces the amount of data needed to fine-tune pre-trained language models for downstream tasks.

Abstract

The paper introduces DEFT-UCS, a data-efficient fine-tuning framework that leverages unsupervised core-set selection to identify a smaller, representative dataset for fine-tuning pre-trained language models (PLMs) on downstream tasks. The key highlights are: DEFT-UCS utilizes unsupervised core-set selection via clustering to find a core-set Dc that is a subset of the original dataset D. This Dc can be used to fine-tune a PLM without compromising performance compared to fine-tuning on the full D. The authors evaluate DEFT-UCS in the context of fine-tuning PLMs for text-editing tasks, comparing to the state-of-the-art text-editing model CoEDIT. They show that DEFT-UCS models can achieve comparable or better performance on multiple text-editing datasets while using only 32.5% of the data used to train CoEDIT. The authors also compare DEFT-UCS to the LIMA approach, which uses a small, manually curated dataset for fine-tuning, and find that DEFT-UCS outperforms LIMA across the evaluated text-editing tasks. The paper provides insights on how the size of the initial dataset Dbase and the sampling method (random, easy, hard) used in the unsupervised core-set selection can impact the performance of the final fine-tuned model. Human evaluation results show that the best DEFT-UCS model is perceived to generate more accurately edited sentences compared to the CoEDIT model, despite using 70% less training data.

Stats

The DEFT-UCS framework is evaluated on 8 different datasets spanning 6 text-editing tasks, including simplification, coherence, clarity, fluency, grammar correction, and neutralization.

Quotes

"DEFT-UCS can utilize only 32.5% of CoEDIT's training data to produce fine-tuned models with improved accuracy on four different text-editing tasks, and comparable accuracy on two text-editing tasks compared to CoEDIT." "Our best DEFT-UCS model requires 70% less data than CoEDIT (Raheja et al., 2023) to generate edited sentences of similar quality and perceived accuracy in comparison to CoEDIT."

Key Insights Distilled From

DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection

by Devleena Das... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2310.16776.pdf

Deeper Inquiries

How can the DEFT-UCS framework be extended to other domains beyond text-editing, such as computer vision or speech processing tasks

The DEFT-UCS framework can be extended to other domains beyond text-editing by adapting the core principles of unsupervised core-set selection to suit the specific requirements of different tasks. For computer vision tasks, the framework can be modified to analyze image embeddings and cluster them based on similarity metrics to identify a representative subset of images for fine-tuning models. Similarly, for speech processing tasks, audio embeddings can be clustered to select a core-set of audio samples for efficient model training. By customizing the data representation and clustering methods to suit the characteristics of the data in these domains, DEFT-UCS can be applied effectively to a wide range of tasks beyond text-editing.

What are the potential limitations of the unsupervised core-set selection approach used in DEFT-UCS, and how can it be further improved to handle more complex datasets and tasks

The unsupervised core-set selection approach used in DEFT-UCS may have limitations when dealing with more complex datasets and tasks. One potential limitation is the reliance on distance metrics in embedding spaces, which may not capture the full complexity of the data distribution. To address this limitation, the approach can be further improved by incorporating more advanced clustering algorithms that can handle non-linear relationships in the data. Additionally, integrating active learning techniques to iteratively refine the core-set selection process based on model feedback can enhance the adaptability of DEFT-UCS to complex datasets. Moreover, exploring ensemble methods that combine multiple core-set selection strategies can help mitigate the limitations of individual approaches and improve the overall performance of the framework.

Given the insights on the impact of initial dataset size and sampling method, how can the hyperparameter selection process in DEFT-UCS be automated to make it more widely applicable

Automating the hyperparameter selection process in DEFT-UCS can enhance its usability and applicability across different tasks. One approach to automate this process is to implement a hyperparameter optimization algorithm, such as Bayesian optimization or grid search, to search for the optimal hyperparameter values based on performance metrics. By defining a search space for the hyperparameters and evaluating the model performance for different combinations, the algorithm can iteratively refine the hyperparameter settings to maximize the model's effectiveness. Additionally, incorporating techniques like cross-validation to validate the selected hyperparameters and ensure robustness can further improve the automation process. By automating hyperparameter selection, DEFT-UCS can be more easily applied to diverse datasets and tasks without the need for manual tuning.