# LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancement

arXiv: https://arxiv.org/abs/2409.15724 · HTML: https://mlsanity.com/paper/2409.15724/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond](https://mlsanity.com/paper/2404.14824/) (2024)
- [Mining User Opinions in Mobile App Reviews: A Keyword-based Approach](https://mlsanity.com/paper/1505.04657/) (2015)
- [The Limits of ChatGPT in Extracting Aspect-Category-Opinion-Sentiment Quadruples: A Comparative Analysis](https://mlsanity.com/paper/2310.06502/) (2023)
- [Listening to Users' Voice: Automatic Summarization of Helpful App Reviews](https://mlsanity.com/paper/2210.06235/) (2022)
- [Fault in your stars: An Analysis of Android App Reviews](https://mlsanity.com/paper/1708.04968/) (2017)
- [The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews](https://mlsanity.com/paper/1810.05187/) (2018)
- [On the Importance of Performing App Analysis Within Peer Groups](https://mlsanity.com/paper/2204.03794/) (2022)
- [Unveiling Competition Dynamics in Mobile App Markets through User Reviews](https://mlsanity.com/paper/2312.01981/) (2023)

## Built on by (most cited first, 4 of 4)

- [The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study](https://mlsanity.com/paper/2507.03156/) (2025)
- [RescueLens: LLM-Powered Triage and Action on Volunteer Feedback for Food Rescue](https://mlsanity.com/paper/2511.15698/) (2025)
- [LikeThis! Empowering App Users to Submit UI Improvement Suggestions Instead of Complaints](https://mlsanity.com/paper/2603.04245/) (2026)
- [Enhancing LLMs through human feedback: a journey towards self-improvement](https://mlsanity.com/paper/2607.11267/) (2026)

## Abstract (arXiv)

The exponential growth of the mobile app market underscores the importance of constant innovation and rapid response to user demands. As user satisfaction is paramount to the success of a mobile application (app), developers typically rely on user reviews, which represent user feedback that includes ratings and comments to identify areas for improvement. However, the sheer volume of user reviews poses challenges in manual analysis, necessitating automated approaches. Existing automated approaches either analyze only the target apps reviews, neglecting the comparison of similar features to competitors or fail to provide suggestions for feature enhancement. To address these gaps, we propose a Large Language Model (LLM)-based Competitive User Review Analysis for Feature Enhancement) (LLM-Cure), an approach powered by LLMs to automatically generate suggestion s for mobile app feature improvements. More specifically, LLM-Cure identifies and categorizes features within reviews by applying LLMs. When provided with a complaint in a user review, LLM-Cure curates highly rated (4 and 5 stars) reviews in competing apps related to the complaint and proposes potential improvements tailored to the target application. We evaluate LLM-Cure on 1,056,739 reviews of 70 popular Android apps. Our evaluation demonstrates that LLM-Cure significantly outperforms the state-of-the-art approaches in assigning features to reviews by up to 13% in F1-score, up to 16% in recall and up to 11% in precision. Additionally, LLM-Cure demonstrates its capability to provide suggestions for resolving user complaints. We verify the suggestions using the release notes that reflect the changes of features in the target mobile app. LLM-Cure achieves a promising average of 73% of the implementation of the provided suggestions.