Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models
This work addresses computational constraints and fine-tuning needs for sentiment analysis with LLMs, offering an incremental improvement in efficiency and performance.
The paper tackled the challenge of adapting large language models (LLMs) to domain-specific sentiment analysis tasks by proposing the Dynamic Adaptive Rank Space Exploration (DARSE) framework, which achieved a 15.1% improvement in MSE and a 4.3% improvement in accuracy compared to previous work.
Sentiment analysis has become increasingly important for assessing public opinion and informing decision-making. Large language models (LLMs) have revolutionized this field by capturing nuanced language patterns. However, adapting LLMs to domain-specific sentiment analysis tasks remains challenging due to computational constraints and the need for optimal fine-tuning. To address these challenges, we propose a novel Dynamic Adaptive Rank Space Exploration (DARSE) framework for efficient and effective sentiment analysis using LLMs. DARSE consists of a coarse-grained greedy algorithm to identify the optimal rank range, a fine-grained exploration algorithm to refine rank selection, and a dynamic rank allocation method to determine the optimal rank combination for each LLM layer. Extensive experiments demonstrate that DARSE significantly improves sentiment analysis accuracy, achieving a 15.1% improvement in MSE and a 4.3% improvement in accuracy compared to previous work. Our framework strikes a balance between computational efficiency and model performance, making it a promising approach for sentiment analysis with LLMs.