Javad SeraJ

Javad SeraJ, Mohammad Mahdi Mohajeri, Mohammad Javad Dousti

Tuning large language models is essential for optimizing their performance across diverse applications, particularly in scenarios with limited data availability. Tuning large language models in scarce data scenarios is crucial, particularly given that the convergence speed of the LoRA method is lower than that of full fine-tuning. In this paper, we present an analysis of post-training methods including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Odds Ratio Preference Optimization (ORPO) within the context of task-specific learning using the LoRA method. Next we introduce $D^2LoRA$, a data-driven approach for initializing LoRA metrics that enhances training efficiency, especially in limited-data settings. Our experiments compare $D^2LoRA$ with vanilla LoRA in terms of performance and catastrophic forgetting under extremely data-constrained conditions. The results demonstrate that $D^2LoRA$ achieves a 1% improvement GSM8K benchmark and a 2-point improvement in ROUGE score in title generation tasks. $D^2LoRA$ facilitates the adaptation of LLMs to multiple tasks even when task-specific data is scarce, thereby reducing training expenses and offering data cost.

Javad Seraj, Mohammad Mahdi Mohajeri, Mohammad Javad Dousti et al.

Automatic evaluation by large language models (LLMs) is a prominent topic today; however, judgment and evaluation tasks are often subjective and influenced by various factors, making adaptation challenging. While many studies demonstrate the capabilities of state-of-the-art proprietary LLMs in comparison to human evaluators, they often struggle to adapt to reference evaluators over time, a requirement for achieving personalized judgment. Additionally, numerous works have attempted to apply open LLMs as judges or evaluators, but these efforts frequently overlook the limitations of working with scarce data. Personalized judgment is inherently associated with limited data scenarios, which are common in many real-world problems. Our work aims to present a data augmentation technique to select a more effective sample from limited data in order to align an open LLM with human preference. Our work achieves approximately 7% improvements in Pearson correlation with a reference judge over the baseline,and 30% improvement over the base model (Llama3.1-8B-Instruct) in the mathematical reasoning evaluation task. demonstrating that augmenting selecting more effective preference data enables our approach to surpass baseline methods.