FeedAIde: Guiding App Users to Submit Rich Feedback Reports by Asking Context-Aware Follow-Up Questions
- URL: http://arxiv.org/abs/2603.04244v1
- Date: Wed, 04 Mar 2026 16:31:55 GMT
- Title: FeedAIde: Guiding App Users to Submit Rich Feedback Reports by Asking Context-Aware Follow-Up Questions
- Authors: Ali Ebrahimi Pourasad, Meyssam Saghiri, Walid Maalej,
- Abstract summary: We propose FeedAIde, a context-aware, interactive feedback approach.<n>FeedAIde captures contextual information, such as the screenshot where the issue emerges.<n>It uses it for adaptive follow-up questions to collaboratively refine with the user a rich feedback report.
- Score: 6.85181982998051
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: User feedback is essential for the success of mobile apps, yet what users report and what developers need often diverge. Research shows that users often submit vague feedback and omit essential contextual details. This leads to incomplete reports and time-consuming clarification discussions. To overcome this challenge, we propose FeedAIde, a context-aware, interactive feedback approach that supports users during the reporting process by leveraging the reasoning capabilities of Multimodal Large Language Models. FeedAIde captures contextual information, such as the screenshot where the issue emerges, and uses it for adaptive follow-up questions to collaboratively refine with the user a rich feedback report that contains information relevant to developers. We implemented an iOS framework of FeedAIde and evaluated it on a gym's app with its users. Compared to the app's simple feedback form, participants rated FeedAIde as easier and more helpful for reporting feedback. An assessment by two industry experts of the resulting 54 reports showed that FeedAIde improved the quality of both bug reports and feature requests, particularly in terms of completeness. The findings of our study demonstrate the potential of context-aware, GenAI-powered feedback reporting to enhance the experience for users and increase the information value for developers.
Related papers
- User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal [59.120335322495436]
We analyze user feedback in the user-LLM conversation logs, providing insights into when and why such feedback occurs.<n>Second, we study harvesting learning signals from such implicit user feedback.
arXiv Detail & Related papers (2025-07-30T23:33:29Z) - Mobile Application Review Summarization using Chain of Density Prompting [1.90298817989995]
We leverage Large Language Models (LLMs) to summarize mobile app reviews.<n>We use the Chain of Density (CoD) prompt to guide OpenAI GPT-4 to generate abstractive, semantically dense, and easily interpretable summaries.
arXiv Detail & Related papers (2025-06-17T05:17:21Z) - Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User [117.82681846559909]
Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations.<n>We propose a generative reward model based simulated user, named GRSU, for automatic interaction with CRSs.
arXiv Detail & Related papers (2025-04-29T06:37:30Z) - From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go [21.811104609265158]
We developed Vocalizer, a mobile application that enables users to provide reviews through voice input.<n>Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews.<n>We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online.
arXiv Detail & Related papers (2024-12-06T21:59:47Z) - Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback.
The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied.
We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
arXiv Detail & Related papers (2024-04-19T16:45:50Z) - AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models [34.82568259708465]
Allhands is an innovative analytic framework designed for large-scale feedback analysis through a natural language interface.
LLMs are large language models that enhance accuracy, robustness, generalization, and user-friendliness.
Allhands delivers comprehensive multi-modal responses, including text, code, tables, and images.
arXiv Detail & Related papers (2024-03-22T12:13:16Z) - RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models [17.782410287625645]
This paper proposes a benchmark, RefuteBench, covering tasks such as question answering, machine translation, and email writing.
The evaluation aims to assess whether models can positively accept feedback in form of refuting instructions and whether they can consistently adhere to user demands throughout the conversation.
arXiv Detail & Related papers (2024-02-21T01:39:56Z) - Continually Improving Extractive QA via Human Feedback [59.49549491725224]
We study continually improving an extractive question answering (QA) system via human user feedback.
We conduct experiments involving thousands of user interactions under diverse setups to broaden the understanding of learning from feedback over time.
arXiv Detail & Related papers (2023-05-21T14:35:32Z) - Advances and Challenges in Conversational Recommender Systems: A Survey [133.93908165922804]
We provide a systematic review of the techniques used in current conversational recommender systems (CRSs)
We summarize the key challenges of developing CRSs into five directions.
These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI)
arXiv Detail & Related papers (2021-01-23T08:53:15Z) - Automating App Review Response Generation [67.58267006314415]
We propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses.
Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4.
arXiv Detail & Related papers (2020-02-10T05:23:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.