تقرير
Self-Improving Customer Review Response Generation Based on LLMs
| العنوان: | Self-Improving Customer Review Response Generation Based on LLMs |
|---|---|
| المؤلفون: | Azov, Guy, Pelc, Tatiana, Alon, Adi Fledel, Kamhi, Gila |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: 18 pages, 4 figure, 8 figures in Appendix, accepted to LREC-COLING 2024 workshop |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.03845 |
| رقم الأكسشن: | edsarx.2405.03845 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |