Apple wants AI to be more personal and write in your style, research paper reveals

India Today

20-06-2025

If you have ever asked an AI to write an email or a message, you have likely noticed a familiar flaw: the results often sound too generic. Even with detailed instructions, large language models like ChatGPT or Gemini often struggle to reflect a person's actual tone or writing style. Apple now believes it has found a solution. In a new research paper to be presented at the International Conference on Machine Learning (ICML 2025), Apple researchers introduced a technique called PROSE, which is short for Preference Reasoning by Observing and Synthesising Examples. It is designed to help AI systems learn directly from a user's writing history, so future outputs can better match the user's natural tone and preferences.advertisementWhat is PROSE and how does it work?Unlike older methods such as prompt engineering or reinforcement learning from human feedback, PROSE builds a unique and evolving profile of how a person writes. It works in two key steps:1. Iterative refinement: The AI compares its generated drafts with actual user-written examples and adjusts its internal style description until the output closely resembles the user's tone.
2. Consistency verification: The system then checks whether the identified writing preferences (like 'use short sentences' or 'be friendly and casual') appear consistently across multiple samples. This ensures that the AI doesn't overly rely on just one piece of writing.The result is a self-learning system that can tailor its future writing based on your overall writing behaviour, not just a single document or prompt.While Apple didn't name any of its products in the research paper, the potential applications are clear. With the company's growing push into Apple Intelligence – its next-gen AI assistant features – tools like PROSE could allow apps like Mail or Notes to generate texts that sound much more like you.advertisementApple's new Foundation Models framework, announced recently, will let developers access Apple's local language models. That means PROSE-style personalisation could eventually power writing tools inside a wide range of third-party apps as well.Alongside PROSE, Apple also introduced a new dataset called PLUME – short for Preference Learning from User Emails and Memos. It replaces an earlier dataset, PRELUDE, and aims to better test how well AI systems can understand and replicate writing preferences.When tested using PLUME, PROSE beat existing personalisation techniques. It outperformed a similar system named CIPHER by 33 per cent and even showed better results than standard in-context learning (ICL) approaches, particularly when used with advanced models like GPT-4o. Interestingly, combining PROSE with ICL delivered the best overall results – with up to a 9 per cent improvement over ICL alone.Why personalised AI like PROSE matter Apple's PROSE research isn't happening in isolation, it follows another critical finding from the company's AI team that raises serious questions about how reliable today's large language models (LLMs) really are, especially when faced with complex tasks.In a separate paper titled The Illusion of Thinking, which was released earlier this month, Apple researchers argue that even top-tier models like ChatGPT o3, Claude 3.7 Sonnet, and DeepSeek-R1 may not be as intelligent as they seem. These models are often described as 'Large Reasoning Models' (LRMs) because they attempt to break down complicated tasks using a step-by-step 'chain of thought.' But Apple's study suggests this ability might be more fragile than expected. To test this, the researchers created custom environments — including puzzles like the Tower of Hanoi, River Crossing, and Blocks World — designed to gradually increase the complexity of the problems. The aim wasn't just to test whether the AI could get the right answer, but whether it could reason its way there logically.advertisementThe results were surprising. When tasks were simple, traditional models without complex reasoning strategies often performed better. At a medium level of complexity, reasoning-based models did briefly take the lead. But as complexity increased further, all models -- including the most advanced -- experienced what the researchers call a 'performance collapse.' In short, the AI systems that seemed smart at first struggled or gave up entirely when the problems got too hard -- despite having enough computing power to continue.This highlights why Apple's PROSE approach is timely. If AI can't truly 'think' through tough challenges yet, then at the very least, it should be able to sound more human -- by learning how you write.