Persona- SQ : A Personalized Suggested Question Generation Framework For Real-world Documents (2025)

Abstract: Suggested questions (SQs) provide an effective initial interface for users to engage with their documents in AI-powered reading applications. In practical reading sessions, users have diverse backgrounds and reading goals, yet current SQ features typically ignore such user information, resulting in homogeneous or ineffective questions. We introduce a pipeline that generates personalized SQs by incorporating reader profiles (professions and reading goals) and demonstrate its utility in two ways: 1) as an improved SQ generation pipeline that produces higher quality and more diverse questions compared to current baselines, and 2) as a data generator to fine-tune extremely small models that perform competitively with much larger models on SQ generation. Our approach can not only serve as a drop-in replacement in current SQ systems to immediately improve their performance but also help develop on-device SQ models that can run locally to deliver fast and private SQ experience.

## introduction large language models (llms) have shown strong promise as document assistants to help users better read and understand their content in the form of ai-powered reading software and applications such as chatpdf, 1 notebooklm, 2 and acrobat's ai assistant. 3 one of the core features of these ai-powered reading applications is automatically generating suggested questions (sqs) @xcite . these questions are among the first features that users see when they first upload a document and have the potential to help improve user engagement @xcite , and guide the user to more effectively navigate documents @xcite , ultimately leading to improved productivity. these automatically generated sqs could also relieve users from manually typing questions they want to ask, resulting in a more effortless interaction @xcite . typically, users with different backgrounds and interests may possess distinct goals and information-seeking needs, even when reading the same document. ideally, for different users, the ai-powered reading applications would tailor the generated sqs to their backgrounds and needs. unfortunately, the current sq feature across reading applications relies mostly on the document as the anchor for generating document-relevant sqs but largely ignores information about users themselves. one challenge lies in the difficulty of obtaining such user profile information during reading, likely because of privacy considerations and because user activities, from which we can draw inferences about the user, are difficult to track and record, especially when the document is in the form of a pdf. as a result, without user information, the generated sqs may appear homogeneous, repetitive, and ineffective. these observations and challenges motivate our work: how to personalize the generated sqs to tailor to the backgrounds and reading goals of different individuals, especially with the absence of user profile information? ## persona-sq framework we now introduce persona-sq, our approach to generate personalized sqs, which is illustrated in figure 1 . persona-sq consists of five steps: document collection, persona generation, question formulation, and robust quality control mechanisms for filtering suboptimal personas and questions. we show how to use our pipeline in appendix b. ## demonstration 1: persona-sq improves llm-based sq generation in this section, we demonstrate the efficacy of persona-sq when it is instantiated with the state-ofthe-art llm. we show that persona-sq improves sq generation compared to the regular sq generation pipeline without persona information. we perform automatic evaluation on a large set of documents and generate sqs in section 3.1 and human evaluation on a small set of documents in section ## demonstration 2: persona-sq results in powerful small model for sq generation we additionally demonstrate persona-sq's utility in generating synthetic training data to fine-tune extremely small models (less than 400 million parameters) for the task of sq generation. the reason for choosing models of such extreme small scale is twofold. first, the smaller the model size, the easier it is to implement and run the model within an ai-powered reading application in an actual production environment (more in appendix f). second, there is a growing interest in finding practical use cases for extremely small models. 4 both of these motivate us to focus on scaling down model sizes and to contribute a new practical use case, i.e., sq generation, for these small models. we build this model demo with gradio (appendix a). dataset. we instantiate persona-sq with an open-source llm, namely llama-3.1-70b, and apply it on a large set of diverse documents to generate between 9 and 16 sqs per document. we split the dataset according to document ids into training, validation, and test sets. deployment considerations given its tiny size, the model takes only about 760 megabytes on5 related work ## conclusion we introduced persona-sq, an approach to improve suggested questions (sqs) in ai-powered reading applications by incorporating synthetic user profiles consisting of professions and reading goals. through extensive experiments on documents from diverse domains, we demonstrated that persona-sq improves sq quality and diversity compared to traditional non-personalized approaches. we further showed that persona-sq can be used to generate synthetic training data to fine-tune extremely small models (360m parameters) that perform competitively with much larger models on sq generation. these results suggest two promising directions for improving current aipowered reading applications: 1) as an immediate drop-in upgrade to existing cloud-based sq generators to produce more diverse and targeted questions, and 2) as a pipeline to train small, efficient models that can generate high-quality personalized sqs directly on users' devices. we hope our work spurs further research into making ai-powered reading assistants more personalized and accessible.