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    <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.</abstract>
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%0 Conference Proceedings
%T Self-Improving Customer Review Response Generation Based on LLMs
%A Azov, Guy
%A Pelc, Tatiana
%A Fledel Alon, Adi
%A Kamhi, Gila
%Y Malmasi, Shervin
%Y Fetahu, Besnik
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F azov-etal-2024-self
%X 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.
%U https://aclanthology.org/2024.ecnlp-1.5
%P 40-57

Markdown (Informal)

[Self-Improving Customer Review Response Generation Based on LLMs](https://aclanthology.org/2024.ecnlp-1.5) (Azov et al., ECNLP-WS 2024)

• Self-Improving Customer Review Response Generation Based on LLMs (Azov et al., ECNLP-WS 2024)

ACL

• Guy Azov, Tatiana Pelc, Adi Fledel Alon, and Gila Kamhi. 2024. Self-Improving Customer Review Response Generation Based on LLMs. In Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024, pages 40–57, Torino, Italia. ELRA and ICCL.