The emergence of large language models (LLMs) has significantly transformed natural language processing (NLP), enabling more generalized models to perform various tasks with minimal training. However, traditional sentiment analysis methods, which focus on individual tasks such as sentiment classification or aspect-based analysis, are not practical for real-world applications that usually require handling multiple tasks. While offering flexibility, LLMs in sentiment-specific tasks often fall short of the required accuracy. Techniques like fine-tuning and evolutionary model merging help integrate models into a unified framework, which can improve the learning performance while reducing computational costs. The use of task meta-data and curriculum learning to optimize learning processes remains underexplored, while sentiment analysis is a critical task in NLP that requires high accuracy and scalability across multiple subtasks. In this study, we propose a hybrid learning model called Multi-stage Evolutionary Model Merging with Meta data driven Curriculum Learning (MEM-MCL), to enhance the sentiment analysis in large language modeling. In particular, expert models are created through instruction tuning for specific sentiment tasks and then merged using evolutionary algorithms to form a unified model. The merging process is optimized with weak data to enhance performance across tasks. The curriculum learning is incorporated to provide a learning sequence based on task difficulty, improving knowledge extraction from LLMs. Experiment results demonstrate that the proposed MEM-MCL model outperforms conventional LLMs in a majority of sentiment analysis tasks, achieving superior results across various subtasks.

翻译：大语言模型（LLMs）的出现显著改变了自然语言处理（NLP）领域，使得更通用的模型能够以最少的训练执行多种任务。然而，传统的情感分析方法专注于单项任务（如情感分类或基于方面的分析），对于通常需要处理多任务的现实应用并不实用。尽管LLMs在灵活性上具有优势，但在情感专用任务中往往难以达到所需的精度。微调和进化模型融合等技术有助于将模型整合到统一框架中，这可以在降低计算成本的同时提升学习性能。利用任务元数据和课程学习来优化学习过程的研究仍显不足，而情感分析作为NLP中的关键任务，需要在多个子任务上同时具备高精度和可扩展性。在本研究中，我们提出了一种名为“基于元数据驱动课程学习的多阶段进化模型融合”（MEM-MCL）的混合学习模型，以增强大语言模型中的情感分析能力。具体而言，我们首先通过指令微调针对特定情感任务创建专家模型，随后利用进化算法将其融合形成一个统一模型。融合过程通过弱监督数据进行优化，以提升跨任务性能。同时，我们引入课程学习机制，根据任务难度提供学习序列，从而改善从LLMs中提取知识的效果。实验结果表明，所提出的MEM-MCL模型在大多数情感分析任务中优于传统LLMs，并在各项子任务上取得了更优的结果。