Personalized text generation is an emerging research area that has attracted much attention in recent years. Most studies in this direction focus on a particular domain by designing bespoke features or models. In this work, we propose a general approach for personalized text generation using large language models (LLMs). Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation. In writing instruction, the task of writing from sources is often decomposed into multiple steps that involve finding, evaluating, summarizing, synthesizing, and integrating information. Analogously, our approach to personalized text generation consists of multiple stages: retrieval, ranking, summarization, synthesis, and generation. In addition, we introduce a multitask setting that helps the model improve its generation ability further, which is inspired by the observation in education that a student's reading proficiency and writing ability are often correlated. We evaluate our approach on three public datasets, each of which covers a different and representative domain. Our results show significant improvements over a variety of baselines.

I'm looking for help/direction for the use of a text classification engine powered by universal taxonomy in making certain ML processes more efficient through providing context to entities extracted from a corpus in real time. My company, eContext, has curated a universal taxonomy over the past nine years that encompasses everything commercially and socially relevant on the web. It is made up of 650M real user search queries bucketed into 25 vertical categories (Auto, Health, Finance, etc.) containing roughly 450K sub-categories. It's a rule-based system, and we use NLP and nGram chunking to parse long and short form text and map search queries, social posts, web content, blogs, forums, reviews, etc. to the category hierarchy providing structured, topical intelligence to data streams at scale. It is extremely accurate because we've built 55M controlled vocabularies (Ex.