Implicit sentiment analysis (ISA) presents significant challenges due to the absence of salient cue words. Previous methods have struggled with insufficient data and limited reasoning capabilities to infer underlying opinions. Integrating multi-task learning (MTL) with large language models (LLMs) offers the potential to enable models of varying sizes to reliably perceive and recognize genuine opinions in ISA. However, existing MTL approaches are constrained by two sources of uncertainty: data-level uncertainty, arising from hallucination problems in LLM-generated contextual information, and task-level uncertainty, stemming from the varying capacities of models to process contextual information. To handle these uncertainties, we introduce MT-ISA, a novel MTL framework that enhances ISA by leveraging the generation and reasoning capabilities of LLMs through automatic MTL. Specifically, MT-ISA constructs auxiliary tasks using generative LLMs to supplement sentiment elements and incorporates automatic MTL to fully exploit auxiliary data. We introduce data-level and task-level automatic weight learning (AWL), which dynamically identifies relationships and prioritizes more reliable data and critical tasks, enabling models of varying sizes to adaptively learn fine-grained weights based on their reasoning capabilities. We investigate three strategies for data-level AWL, while also introducing homoscedastic uncertainty for task-level AWL. Extensive experiments reveal that models of varying sizes achieve an optimal balance between primary prediction and auxiliary tasks in MT-ISA. This underscores the effectiveness and adaptability of our approach.

翻译：隐式情感分析由于缺乏显著的情感线索词而面临重大挑战。先前的方法因数据不足和推理能力有限而难以推断潜在观点。将多任务学习与大语言模型相结合，有望使不同规模的模型在隐式情感分析中可靠地感知和识别真实观点。然而，现有的多任务学习方法受到两种不确定性来源的制约：数据级不确定性（源于大语言模型生成上下文信息时的幻觉问题）和任务级不确定性（源于模型处理上下文信息的能力差异）。为处理这些不确定性，我们提出了MT-ISA——一种新颖的多任务学习框架，该框架通过自动多任务学习机制，利用大语言模型的生成与推理能力来增强隐式情感分析。具体而言，MT-ISA利用生成式大语言模型构建辅助任务以补充情感要素，并结合自动多任务学习以充分利用辅助数据。我们引入了数据级与任务级自动权重学习机制，该机制能动态识别数据关系，优先处理更可靠的数据和关键任务，使不同规模的模型能够基于其推理能力自适应地学习细粒度权重。我们研究了三种数据级自动权重学习策略，同时为任务级自动权重学习引入了同方差不确定性度量。大量实验表明，不同规模的模型在MT-ISA中实现了主要预测任务与辅助任务之间的最优平衡，这验证了我们方法的有效性与适应性。