mlsanityData to 7 Oct 2026

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

arXiv 2412.12445 ↗First posted 17 Dec 2024cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

17 of 35 references.

References · 17

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 820
  4. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  5. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
  6. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  7. 2023Character-LLM: A Trainable Agent for Role-Playingcited by 335
  8. 2021CUAD: An Expert-Annotated NLP Dataset for Legal Contract Reviewcited by 232
  9. 2024LongLaMP: A Benchmark for Personalized Long-form Text Generationcited by 91
  10. 2022Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehensioncited by 79
  11. 2024Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinementcited by 47
  12. 2023Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Personacited by 24
5 more
  1. 2022Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generationcited by 17
  2. 2024QGEval: Benchmarking Multi-dimensional Evaluation for Question Generationcited by 17
  3. 2024Improving Socratic Question Generation using Data Augmentation and Preference Optimizationcited by 15
  4. 2024Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinementcited by 14
  5. 2023Evaluation of Question Generation Needs More Referencescited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QAcited by 0
  2. 2026From Profiles to Steering Vectors: Global Sparse Priors and Local Semantic Calibration for Personalized Text Generationcited by 0
  3. 2026How Much Do Legal RAG Systems Still Hallucinate?cited by 0