Investigating the Number of Labelled Samples Required for Specialized Small Language Models to Outperform General Large Language Models on Text Classification Tasks

Core Concepts

Specialized small language models obtained through fine-tuning or instruction-tuning can outperform general large language models used in zero-shot or few-shot settings with only a small number of labelled samples (10-1000), with the exact number depending on dataset characteristics and performance variance.

Abstract

The paper investigates the impact of the number of available labelled training samples on the performance and comparison of different approaches for data-efficient learning in NLP, including fine-tuning, prompting, in-context learning, and instruction-tuning. The goal is to determine how many labelled samples are needed for specialized small language models to outperform their general larger counterparts.
The key findings are:

Specialized small models often need only 10-1000 labelled samples to outperform general large models in zero-shot or few-shot settings, with the exact number depending on dataset characteristics. Binary datasets and tasks requiring better language understanding need more samples (up to 5000) compared to multi-class datasets (up to 100).

Instruction-tuned models provide a good balance, achieving near-best performance with only a fraction of the labelled samples required by fine-tuning.

Performance variance, especially from in-context learning and fine-tuning on few samples, has a significant impact, increasing the required number of labelled samples by 100-200% on average, and up to 1500% in specific cases.

Larger models do not consistently lead to better performance across approaches. Smaller models can sometimes outperform larger ones, especially in prompting, in-context learning, and instruction-tuning.

The paper provides recommendations on when to use general models (quick prototyping, limited budget) versus specialized models (large annotation budget, classification tasks), and highlights the importance of considering performance variance when comparing approaches.

Stats

Specialized small models often need only 10-1000 labelled samples to outperform general large models.
Binary datasets and tasks requiring better language understanding need up to 5000 labelled samples, compared to up to 100 for multi-class datasets.
Performance variance increases the required number of labelled samples by 100-200% on average, and up to 1500% in specific cases.

Quotes

"Specialized small models can outperform general large models using only small number of labelled examples."
"The number of required labelled samples is dependent on the dataset characteristics, with binary datasets and datasets that require better language understanding requiring significantly more labelled samples for the specialised models to outperform the general ones."
"Performance variance has a significant effect on the break-even points between models, increasing the number of required labelled samples by a large amount."

Key Insights Distilled From

Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance

by Branislav Pe... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2402.12819.pdf

Deeper Inquiries

How would the findings change if the investigation was extended to other types of tasks beyond text classification, such as generation tasks

If the investigation were extended to other types of tasks beyond text classification, such as generation tasks, the findings might vary due to the different nature of the tasks. For generation tasks, specialized models may need to be fine-tuned or instruction-tuned differently to optimize performance. In generation tasks, the quality of the generated output, coherence, and relevance to the input may play a more significant role in determining the effectiveness of specialized models compared to general models. Additionally, the complexity of the generation task, the diversity of the output required, and the need for context retention could impact the number of labeled samples needed for specialized models to outperform general models. The performance variance in generation tasks may also be influenced by factors such as the length of the generated text, the diversity of the training data, and the complexity of the language patterns required for accurate generation.

What other factors beyond dataset characteristics and performance variance could influence the comparison between specialized and general models

Beyond dataset characteristics and performance variance, several other factors could influence the comparison between specialized and general models. One key factor is the model architecture and size, as larger models may have more parameters and capacity to capture complex patterns in the data, potentially leading to better performance. The choice of hyperparameters, such as learning rate, batch size, and optimization algorithms, could also impact the comparison, as they affect the training dynamics and convergence of the models. The quality of the pre-training data and the effectiveness of the pre-training process could influence how well a model generalizes to new tasks with limited labeled data. Additionally, the quality of the prompt or instruction used for fine-tuning or instruction-tuning could significantly impact the performance of specialized models. Other factors like the choice of evaluation metrics, the computational resources available, and the expertise of the researchers conducting the experiments could also play a role in the comparison between specialized and general models.

How could the instruction-tuning approach be further improved to provide even better performance with fewer labelled samples

To further improve the instruction-tuning approach and provide better performance with fewer labeled samples, several strategies could be implemented.

Optimized Prompt Design: Developing more sophisticated and task-specific prompts that effectively guide the model to learn the desired task could enhance the performance of instruction-tuned models. Experimenting with different prompt formats, lengths, and structures tailored to the specific task could lead to better results.
Fine-tuning Hyperparameters: Fine-tuning the hyperparameters of the instruction-tuning process, such as the learning rate, batch size, and number of training epochs, could help optimize the training procedure and improve model performance with limited labeled data.
Data Augmentation: Incorporating data augmentation techniques to artificially increase the size of the labeled dataset could provide the model with more diverse examples to learn from, potentially enhancing its generalization capabilities.
Transfer Learning: Leveraging transfer learning techniques by pre-training the model on related tasks or domains before instruction-tuning for the target task could help the model capture more nuanced patterns and improve performance with fewer labeled samples.
Regularization Techniques: Applying regularization techniques like dropout, weight decay, or early stopping during the instruction-tuning process could prevent overfitting and improve the model's ability to generalize to unseen data.
Ensemble Methods: Utilizing ensemble methods by combining multiple instruction-tuned models or incorporating general models into the ensemble could enhance the robustness and performance of the final model.