  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.
