Categories: FAANG

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…
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Recent Posts

Some recent Chroma renders

Model: https://huggingface.co/silveroxides/Chroma-GGUF/blob/main/chroma-unlocked-v38-detail-calibrated/chroma-unlocked-v38-detail-calibrated-Q8_0.gguf Workflow: https://huggingface.co/lodestones/Chroma/resolve/main/simple_workflow.json Prompts used: High detail photo showing an abandoned Renaissance painter’s studio…

5 hours ago

A Gentle Introduction to Multi-Head Latent Attention (MLA)

This post is divided into three parts; they are: • Low-Rank Approximation of Matrices •…

5 hours ago

Converting Pandas DataFrames to PyTorch DataLoaders for Custom Deep Learning Model Training

Pandas DataFrames are powerful and versatile data manipulation and analysis tools.

5 hours ago

Securing America’s Defense Industrial Base

Palantir FedStart and the Path to CMMC ComplianceSecuring the Defense Industrial BaseNever has the imperative…

5 hours ago

No-code data preparation for time series forecasting using Amazon SageMaker Canvas

Time series forecasting helps businesses predict future trends based on historical data patterns, whether it’s…

5 hours ago

Beyond static AI: MIT’s new framework lets models teach themselves

MIT researchers developed SEAL, a framework that lets language models continuously learn new knowledge and…

6 hours ago