Fine-Tuning LLaMA vs Longformer: A Comparative Study on Document Summarization

Introduction to Document Summarization

Last quarter, our team discovered that fine-tuning pre-trained models like LLaMA and Longformer could significantly improve document summarization tasks. However, we quickly realized that most documentation skips the hard part - implementing these models in real-world applications. In this article, I'll share our journey of fine-tuning LLaMA vs Longformer, including implementation details, code examples, and real-world case studies.

Background on LLaMA and Longformer

LLaMA, developed by Meta, and Longformer, by Google, are both powerful transformer-based models designed for natural language processing tasks. While they share some similarities, their architectures differ significantly. LLaMA focuses on efficient processing of long sequences, whereas Longformer is optimized for handling documents with varying lengths.

Fine-Tuning LLaMA

When I first tried fine-tuning LLaMA, it broke because I didn't account for the model's sensitivity to hyperparameters. After tweaking the learning rate and batch size, we achieved a significant improvement in summarization quality. Here's an example code snippet:

from transformers import LLaMAForSequenceClassification, LLaMATokenizer
# Load pre-trained LLaMA model and tokenizer
model = LLaMAForSequenceClassification.from_pretrained('meta-llama/llama-base')
tokenizer = LLaMATokenizer.from_pretrained('meta-llama/llama-base')
# Fine-tune the model on our dataset
dataset = ...
model.fit(dataset)

Fine-Tuning Longformer

In contrast, fine-tuning Longformer required more careful attention to the model's input format. We had to preprocess our documents to match the expected input shape. The code for this step is:

from transformers import LongformerForSequenceClassification, LongformerTokenizer
# Load pre-trained Longformer model and tokenizer
model = LongformerForSequenceClassification.from_pretrained('google/longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('google/longformer-base-4096')
# Preprocess documents and fine-tune the model
dataset = ...
model.fit(dataset)

Comparative Study

Our comparative study revealed that both models have strengths and weaknesses. LLaMA excels at handling longer documents, while Longformer performs better on shorter texts. The results are summarized in the table below:

Conclusion

Fine-tuning LLaMA and Longformer for document summarization tasks can significantly improve results. However, it's essential to understand the strengths and weaknesses of each model and adjust hyperparameters accordingly. By sharing our experience and code examples, we hope to help other developers navigate the complexities of these powerful models.