# AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL

arXiv: https://arxiv.org/abs/2208.12461 · HTML: https://mlsanity.com/paper/2208.12461/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://mlsanity.com/paper/2106.10502/) (2021)
- [Few-Shot NLG with Pre-Trained Language Model](https://mlsanity.com/paper/1904.09521/) (2019)
- [An Interpretable Reasoning Network for Multi-Relation Question Answering](https://mlsanity.com/paper/1801.04726/) (2018)
- [Table-to-Text: Describing Table Region with Natural Language](https://mlsanity.com/paper/1805.11234/) (2018)
- [Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus](https://mlsanity.com/paper/1603.06807/) (2016)
- [Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks](https://mlsanity.com/paper/2004.06015/) (2020)
- [Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types](https://mlsanity.com/paper/1802.06842/) (2018)
- [Knowledge Questions from Knowledge Graphs](https://mlsanity.com/paper/1610.09935/) (2016)
- [Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases](https://mlsanity.com/paper/2010.03157/) (2020)
- [Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss](https://mlsanity.com/paper/1910.13108/) (2019)
- [Question Generation from a Knowledge Base with Web Exploration](https://mlsanity.com/paper/1610.03807/) (2016)

## Built on by (most cited first, 8 of 8)

- [Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation](https://mlsanity.com/paper/2310.08395/) (2023)
- [Large Language Models Can Better Understand Knowledge Graphs Than We Thought](https://mlsanity.com/paper/2402.11541/) (2024)
- [Diversifying Question Generation over Knowledge Base via External Natural Questions](https://mlsanity.com/paper/2309.14362/) (2023)
- [SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation](https://mlsanity.com/paper/2404.01923/) (2024)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [HopPG: Self-Iterative Program Generation for Multi-Hop Question Answering over Heterogeneous Knowledge](https://mlsanity.com/paper/2308.11257/) (2023)
- [Can LLMs Ask Good Questions?](https://mlsanity.com/paper/2501.03491/) (2025)
- [Q2Forge: Minting Competency Questions and SPARQL Queries for Question-Answering Over Knowledge Graphs](https://mlsanity.com/paper/2505.13572/) (2025)

## Abstract (arXiv)

This study investigates the task of knowledge-based question generation (KBQG). Conventional KBQG works generated questions from fact triples in the knowledge graph, which could not express complex operations like aggregation and comparison in SPARQL. Moreover, due to the costly annotation of large-scale SPARQL-question pairs, KBQG from SPARQL under low-resource scenarios urgently needs to be explored. Recently, since the generative pre-trained language models (PLMs) typically trained in natural language (NL)-to-NL paradigm have been proven effective for low-resource generation, e.g., T5 and BART, how to effectively utilize them to generate NL-question from non-NL SPARQL is challenging. To address these challenges, AutoQGS, an auto-prompt approach for low-resource KBQG from SPARQL, is proposed. Firstly, we put forward to generate questions directly from SPARQL for the KBQG task to handle complex operations. Secondly, we propose an auto-prompter trained on large-scale unsupervised data to rephrase SPARQL into NL description, smoothing the low-resource transformation from non-NL SPARQL to NL question with PLMs. Experimental results on the WebQuestionsSP, ComlexWebQuestions 1.1, and PathQuestions show that our model achieves state-of-the-art performance, especially in low-resource settings. Furthermore, a corpus of 330k factoid complex question-SPARQL pairs is generated for further KBQG research.