An LLM-Based Approach to Review Summarization on the App Store
Ratings and reviews are an invaluable resource for users exploring an app on the App Store, providing insights into how others have experienced the app. With review summaries now available in iOS 18.4, users can quickly get a high-level overview of what other users think about an app, while still having the option to dive into individual reviews for more detail. This feature is powered by a novel, multi-step LLM-based system that periodically summarizes user reviews. Our goal in producing review summaries is to ensure they are inclusive, balanced, and accurately reflect the user’s voice. To…
Large-scale commercial search systems optimize for relevance to drive successful sessions that help users find what they are looking for. To maximize relevance, we leverage two complementary objectives: behavioral relevance (results users tend to click or download) and textual relevance (a result’s semantic fit to the query). A persistent challenge…
This paper was accepted at IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2024 Programmers frequently engage with machine learning tutorials in computational notebooks and have been adopting code generation technologies based on large language models (LLMs). However, they encounter difficulties in understanding and working with code produced by…