Leveraging Unlabeled Data to Enhance Fine-Tuning of Large Language Models

Core Concepts

Selecting the most informative unlabeled data samples to pre-fine-tune a pre-trained language model can significantly improve its performance on target tasks, while minimizing the need for costly domain-specific labeled data.

Abstract

The paper introduces a two-stage fine-tuning approach for large language models (LLMs) to leverage vast, unlabeled open data to enhance performance on target tasks. The key idea is to select a subset of unlabeled data that can effectively shift the pre-training distribution of the LLM closer to the target task distribution, rather than just matching the target distribution. The authors propose a scalable data selection method called GOT-D (Gradients of Optimal Transport for Data Selection) that leverages the gradients of the Optimal Transport (OT) distance between the candidate unlabeled data and the target task data. This allows the selected data to optimally prepare the pre-trained model for the target task, even in low-data regimes. The authors demonstrate the effectiveness of GOT-D across diverse tasks, including model detoxification, domain-specific NLU tasks, and general GLUE benchmarks. Compared to existing data selection methods, GOT-D consistently achieves the best performance, especially with limited selection budgets. It also scales efficiently to millions of samples, completing the selection within a single GPU hour. The paper highlights the potential of cost-effective fine-tuning by leveraging unlabeled data, making the benefits of fine-tuning more accessible. The proposed two-stage fine-tuning approach and the GOT-D data selection method lay the groundwork for practical and efficient adaptation of pre-trained LLMs to various applications.

Stats

Fine-tuning a 175B GPT-3 model on 100K short samples can cost $1,500 using OpenAI's API. GPT-2 (base, 124M) is used as the base model for the model detoxification task. BERT-base-uncased is used as the base model for the domain-specific NLU tasks and the GLUE benchmark.

Quotes

"While expert-annotated safety datasets would provide an ideal solution, their acquisition is both costly and time-intensive. A pragmatic alternative, as illustrated in Fig. 2, is to first extract relevant samples from the vast pool of open, unlabeled data and fine-tune the pre-trained model on these samples." "Our key idea is to prioritize samples that most effectively shift the pre-training distribution closer to the target data distribution. Intuitively, fine-tuning a pre-trained model with such samples would boost its performance on the target dataset."

Key Insights Distilled From

Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs

by Feiyang Kang... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02774.pdf

Deeper Inquiries

How can the proposed data selection method be extended to handle multiple target tasks simultaneously, where the optimal data selection may need to balance the needs of different tasks?

The proposed data selection method can be extended to handle multiple target tasks simultaneously by incorporating a multi-objective optimization approach. In this scenario, the data selection process would aim to balance the needs of different tasks by optimizing multiple objectives simultaneously. One way to achieve this is by defining a weighted sum of the objectives, where each task's importance is represented by a weight. The data selection algorithm would then aim to minimize a combined objective function that considers the performance improvement for each task weighted by its importance. This would ensure that the selected data effectively improves the performance across all target tasks, taking into account their relative priorities. Additionally, techniques from multi-task learning and transfer learning can be leveraged to jointly train the model on multiple tasks. By fine-tuning the pre-trained model on the selected data for each task, the model can learn to generalize across different tasks while benefiting from task-specific data. This approach would enable the model to adapt to the requirements of multiple tasks simultaneously, enhancing its overall performance across diverse domains.

How can the potential limitations of the assumption that the candidate dataset roughly matches the pre-training distribution be relaxed or verified in practice?

The assumption that the candidate dataset roughly matches the pre-training distribution may have limitations in real-world scenarios where the distribution of the candidate dataset deviates significantly from the pre-training data. To relax or verify this assumption in practice, several strategies can be employed: Distribution Alignment Techniques: Utilize domain adaptation or distribution alignment techniques to align the candidate dataset with the pre-training distribution. Methods such as adversarial training or domain adaptation can help bridge the gap between the candidate dataset and the pre-training data distribution. Data Augmentation: Augment the candidate dataset with synthetic data or samples from related domains to better match the pre-training distribution. Data augmentation techniques can help diversify the candidate dataset and make it more representative of the pre-training data. Domain Analysis: Conduct a thorough analysis of the candidate dataset to understand its domain-specific characteristics and compare them with the pre-training data distribution. This analysis can help identify areas of divergence and guide data selection strategies accordingly. Cross-Validation: Perform cross-validation experiments where the model is trained and evaluated on subsets of the candidate dataset to assess its generalization performance. By validating the model's performance on different subsets, one can gain insights into the alignment between the candidate dataset and the pre-training distribution. Transfer Learning Performance: Evaluate the model's performance after fine-tuning on the selected data and compare it with the performance on a validation set. A significant improvement in performance would indicate that the selected data effectively captures the characteristics of the pre-training distribution. By incorporating these strategies, the assumption of matching distributions between the candidate dataset and the pre-training data can be relaxed or verified, ensuring the effectiveness of the data selection process.

Could the insights from this work on data selection for pre-fine-tuning be applied to other types of pre-trained models beyond language models, such as vision transformers or multimodal models?

Yes, the insights from this work on data selection for pre-fine-tuning can be applied to other types of pre-trained models beyond language models, such as vision transformers or multimodal models. The fundamental principle of selecting data that nudges the pre-training distribution closer to the target distribution can be generalized to various domains, including computer vision and multimodal tasks. Here's how these insights can be applied to different types of pre-trained models: Vision Transformers: For vision transformers, the data selection process can involve selecting images or image patches that align with the target task distribution. By prioritizing samples that bridge the gap between the pre-training image distribution and the target task requirements, the model can be effectively fine-tuned for specific vision tasks. Multimodal Models: In the case of multimodal models that combine text and image inputs, the data selection method can focus on selecting a diverse set of text-image pairs that capture the nuances of the target tasks. By balancing the representation of both modalities in the selected data, the multimodal model can be fine-tuned to perform well on a range of tasks that require integration of text and image information. Transfer Learning: The insights on data selection for pre-fine-tuning can also be extended to transfer learning scenarios where pre-trained models are adapted to new tasks or domains. By selecting data that optimally prepares the model for the target task distribution, the transfer learning process can be more efficient and effective across different types of pre-trained models. Overall, the principles of data selection for pre-fine-tuning can be adapted and applied to a variety of pre-trained models beyond language models, enabling improved performance and generalization across diverse domains and tasks.