Customer feedback is invaluable to companies as they refine their products. Monitoring customer feedback can be automated with Aspect Level Sentiment Classification (ALSC) which allows us to analyse specific aspects of the products in reviews. Large Language Models (LLMs) are the heart of many state-of-the-art ALSC solutions, but they perform poorly in some scenarios requiring Coreference Resolution (CR). In this work, we propose a framework to improve an LLM's performance on CR-containing reviews by fine tuning on highly inferential tasks. We show that the performance improvement is likely attributed to the improved model CR ability. We also release a new dataset that focuses on CR in ALSC.

翻译：客户反馈对于公司改进产品至关重要。方面级情感分类（ALSC）能够自动化地分析评论中产品的具体方面，从而实现对客户反馈的监控。大型语言模型（LLMs）是众多先进ALSC解决方案的核心，但在需要指代消解（CR）的场景中表现欠佳。本研究提出一个框架，通过对高推理任务进行微调来提升LLM在处理包含指代消解的评论时的性能。我们证明，这一性能提升很可能归因于模型指代消解能力的增强。此外，我们还发布了一个专注于ALSC中指代消解问题的新数据集。