CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning

TL;DR

This paper introduces CLLoRA, a method to evaluate how context length and quality influence large language model fine-tuning performance in federated learning, highlighting the importance of context quality over length.

Contribution

The paper presents CLLoRA, a novel approach to measure the effects of context length and quality on LLM fine-tuning in heterogeneous federated learning settings.

Findings

Context quality imbalance affects local and global model performance.

Context length has minimal impact on local training but significantly influences the global model.

Insights into how context factors impact LLM fine-tuning in federated environments.

Abstract

Large language model fine-tuning has been identified as an efficient approach to applying the pre-trained Large language models to other domains. To guarantee data privacy for different data owners, models are often fine-tuned in federated learning environments across different data owners, which often involve data heterogeneity issues and affect the fine-tuning performance. In addition, the length of the context for the training data has been identified as a major factor that affects the LLM's model performance. To efficiently measure how the context length affects the LLM's model performance in heterogeneous federated learning environments, we propose CLLoRA. CLLoRA utilizes the parameter-efficient fine-tuning approach LoRA based on different kinds of LLMs with varying sizes as the fine-tuning approach to investigate whether the quality and length of contexts can serve as standards for measuring non-IID context. The findings indicate that an imbalance in context quality not only affects local training on clients but also impacts the global model's performance. However, context length has a minimal effect on local training but a more significant influence on the global model. These results provide insights into how context quality and length affect the model performance for LLM fine-tuning in federated learning environments.