用大模型标注数据训练轻量模型，低成本实现高精度情感分析
LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction
- 用大模型生成标注数据，指导轻量模型训练
- 仅50个标注样例即达49.85的F1分数，接近大模型性能
- 计算成本低，适合资源受限场景下的情感分析任务
情感分析任务的训练依赖人工标注数据，成本高昂。本文提出LA-ABSA，利用大语言模型生成标注数据，微调轻量级模型完成复杂的方面级情感分析任务。在五个数据集上评估了目标方面情感检测（TASD）和方面情感四元组预测（ASQP）。该方法优于以往的数据增强策略，在低资源场景下表现媲美大模型提示法，同时显著降低能耗。例如，在SemEval Rest16数据集上，仅使用50个示例进行上下文学习（ICL）引导标注，LA-ABSA在ASQP任务中取得49.85的F1分数，接近使用Gemma-3-27B模型的上下文学习提示法（51.10），且所需计算资源大幅减少。
原文摘要 · Abstract (English)
Training models for Aspect-Based Sentiment Analysis (ABSA) tasks requires manually annotated data, which is expensive and time-consuming to obtain. This paper introduces LA-ABSA, a novel approach that leverages Large Language Model (LLM)-generated annotations to fine-tune lightweight models for complex ABSA tasks. We evaluate our approach on five datasets for Target Aspect Sentiment Detection (TASD) and Aspect Sentiment Quad Prediction (ASQP). Our approach outperformed previously reported augmentation strategies and achieved competitive performance with LLM-prompting in low-resource scenarios, while providing substantial energy efficiency benefits. For example, using 50 annotated examples for in-context learning (ICL) to guide the annotation of unlabeled data, LA-ABSA achieved an F1 score of 49.85 for ASQP on the SemEval Rest16 dataset, closely matching the performance of ICL prompting with Gemma-3-27B (51.10), while requiring significantly lower computational resources.
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