# FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation

arXiv: https://arxiv.org/abs/2309.05007 · HTML: https://mlsanity.com/paper/2309.05007/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 55 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia](https://mlsanity.com/paper/1805.05942/) (2018)
- [Answer-based Adversarial Training for Generating Clarification Questions](https://mlsanity.com/paper/1904.02281/) (2019)
- [Recent Advances in Neural Question Generation](https://mlsanity.com/paper/1905.08949/) (2019)
- [Zero-shot Fact Verification by Claim Generation](https://mlsanity.com/paper/2105.14682/) (2021)
- [Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge](https://mlsanity.com/paper/2104.06828/) (2021)
- [Semantic Graphs for Generating Deep Questions](https://mlsanity.com/paper/2004.12704/) (2020)
- [Unsupervised Multi-hop Question Answering by Question Generation](https://mlsanity.com/paper/2010.12623/) (2020)
- [Inquisitive Question Generation for High Level Text Comprehension](https://mlsanity.com/paper/2010.01657/) (2020)
- [ClarQ: A large-scale and diverse dataset for Clarification Question Generation](https://mlsanity.com/paper/2006.05986/) (2020)
- [MixQG: Neural Question Generation with Mixed Answer Types](https://mlsanity.com/paper/2110.08175/) (2021)
- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [Learning to Generate Questions by Learning What not to Generate](https://mlsanity.com/paper/1902.10418/) (2019)
- [Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees](https://mlsanity.com/paper/2209.10492/) (2022)
- [Let's Ask Again: Refine Network for Automatic Question Generation](https://mlsanity.com/paper/1909.05355/) (2019)
- [Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation](https://mlsanity.com/paper/2205.01730/) (2022)
- [Generating Followup Questions for Interpretable Multi-hop Question Answering](https://mlsanity.com/paper/2002.12344/) (2020)
- [Learning to Retrieve Engaging Follow-Up Queries](https://mlsanity.com/paper/2302.10978/) (2023)

## Built on by (most cited first, 12 of 12)

- [BooookScore: A systematic exploration of book-length summarization in the era of LLMs](https://mlsanity.com/paper/2310.00785/) (2023)
- [InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification](https://mlsanity.com/paper/2401.16475/) (2024)
- [Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generation](https://mlsanity.com/paper/2503.16789/) (2025)
- [Follow-up Question Generation For Enhanced Patient-Provider Conversations](https://mlsanity.com/paper/2503.17509/) (2025)
- [Which questions should I answer? Salience Prediction of Inquisitive Questions](https://mlsanity.com/paper/2404.10917/) (2024)
- [CHIMERA: A Knowledge Base of Scientific Idea Recombinations for Research Analysis and Ideation](https://mlsanity.com/paper/2505.20779/) (2025)
- [InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning](https://mlsanity.com/paper/2603.05909/) (2026)
- [Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up Questions](https://mlsanity.com/paper/2502.17715/) (2025)
- [From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM](https://mlsanity.com/paper/2504.05801/) (2025)
- [From Answers to Guidance: A Proactive Dialogue System for Legal Documents](https://mlsanity.com/paper/2510.19723/) (2025)
- [SnappyMeal: Design and Longitudinal Evaluation of a Multimodal AI Food Logging Application](https://mlsanity.com/paper/2511.03907/) (2025)
- [DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering](https://mlsanity.com/paper/2604.15140/) (2026)

## Abstract (arXiv)

Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of real-world information-seeking follow-up question generation (FQG), which aims to generate follow-up questions seeking a more in-depth understanding of an initial question and answer. We construct FOLLOWUPQG, a dataset of over 3K real-world (initial question, answer, follow-up question) tuples collected from a Reddit forum providing layman-friendly explanations for open-ended questions. In contrast to existing datasets, questions in FOLLOWUPQG use more diverse pragmatic strategies to seek information, and they also show higher-order cognitive skills (such as applying and relating). We evaluate current question generation models on their efficacy for generating follow-up questions, exploring how to generate specific types of follow-up questions based on step-by-step demonstrations. Our results validate FOLLOWUPQG as a challenging benchmark, as model-generated questions are adequate but far from human-raised questions in terms of informativeness and complexity.