通过动态调整秩空间，提升大模型情感分析效率与准确率。
Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models
- 基于粗粒度贪心与细粒度探索，自动寻找最优秩范围。
- 相较之前方法，MSE降低15.1%，准确率提升4.3%。
- 适合资源受限下高效部署大模型情感分析任务。
情感分析在评估公众意见和辅助决策中日益重要。大语言模型（LLMs）通过捕捉细微语言模式，彻底改变了该领域。然而，由于计算限制及最优微调需求，将LLMs适配到特定领域的情感分析任务仍具挑战。为此，我们提出一种新型动态自适应秩空间探索（DARSE）框架，实现高效且有效的情感分析。DARSE包含三个部分：粗粒度贪心算法识别最优秩范围，细粒度探索算法优化秩选择，以及动态秩分配机制为每层LLM确定最佳秩组合。大量实验表明，DARSE显著提升了情感分析精度，在均方误差（MSE）上相比以往方法降低15.1%，准确率提升4.3%。该框架在计算效率与模型性能间取得良好平衡，是大模型情感分析的有力方案。
原文摘要 · Abstract (English)
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.
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