Summary of Self-improving Customer Review Response Generation Based on Llms, by Guy Azov et al.
Self-Improving Customer Review Response Generation Based on LLMs
by Guy Azov, Tatiana Pelc, Adi Fledel Alon, Gila Kamhi
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach for automating responses to user reviews in popular apps. Previous studies have shown that proactive interactions with reviews positively impact app users and encourage revised ratings. However, developers face challenges managing high volumes of reviews. To address this, the authors develop SCRABLE, an adaptive customer review response automation system utilizing retrieval-augmented generation (RAG) and Large Language Models (LLMs). The system generates automatic responses by leveraging user-contributed documents and self-optimizing prompts. A judging mechanism based on LLMs evaluates response quality, mirroring a human evaluator’s role. The authors conduct experiments on real-world datasets, showing the method produces high-quality responses, achieving an 8.5% improvement over the baseline. Manual examination of generated responses validates the system’s efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re building a popular app and you need to respond to lots of user reviews. This can be hard work! Previous studies have shown that when you actively engage with users, they tend to rate your app higher. But how do you manage all those reviews? The authors of this paper propose a new system called SCRABLE that helps automate responses to user reviews. They use special language models and algorithms to generate helpful and high-quality responses. In their experiments, they found that their method was very effective, producing responses that were 8.5% better than usual. This is exciting news for app developers! |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
