Self-Improving Customer Review Response Generation Based on LLMs

TL;DR

This paper presents SCRABLE, an adaptive system that uses retrieval-augmented generation and self-optimizing prompts to automatically generate high-quality customer review responses, improving response quality by over 8.5%.

Contribution

The paper introduces SCRABLE, a novel self-improving system for automated customer review response generation leveraging LLMs and a self-judging mechanism.

Findings

Effective response quality improvement over baseline by 8.5%.

Automated scoring mimics human evaluation.

System enhances response relevance and quality.

Abstract

Previous studies have demonstrated that proactive interaction with user reviews has a positive impact on the perception of app users and encourages them to submit revised ratings. Nevertheless, developers encounter challenges in managing a high volume of reviews, particularly in the case of popular apps with a substantial influx of daily reviews. Consequently, there is a demand for automated solutions aimed at streamlining the process of responding to user reviews. To address this, we have developed a new system for generating automatic responses by leveraging user-contributed documents with the help of retrieval-augmented generation (RAG) and advanced Large Language Models (LLMs). Our solution, named SCRABLE, represents an adaptive customer review response automation that enhances itself with self-optimizing prompts and a judging mechanism based on LLMs. Additionally, we introduce an automatic scoring mechanism that mimics the role of a human evaluator to assess the quality of responses generated in customer review domains. Extensive experiments and analyses conducted on real-world datasets reveal that our method is effective in producing high-quality responses, yielding improvement of more than 8.5% compared to the baseline. Further validation through manual examination of the generated responses underscores the efficacy our proposed system.