# Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance

arXiv: https://arxiv.org/abs/2405.17076 · HTML: https://mlsanity.com/paper/2405.17076/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 27 May 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering](https://mlsanity.com/paper/2308.12060/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers](https://mlsanity.com/paper/2202.00120/) (2022)
- [Modern Baselines for SPARQL Semantic Parsing](https://mlsanity.com/paper/2204.12793/) (2022)
- [SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph](https://mlsanity.com/paper/2402.04627/) (2024)
- [Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering](https://mlsanity.com/paper/2308.16622/) (2023)
- [Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?](https://mlsanity.com/paper/2309.17122/) (2023)
- [A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions](https://mlsanity.com/paper/2304.07772/) (2023)
- [REDrm FM: a Filtered and Multilingual Relation Extraction Dataset](https://mlsanity.com/paper/2306.09802/) (2023)
- [SPARQL Generation with Entity Pre-trained GPT for KG Question Answering](https://mlsanity.com/paper/2402.00969/) (2024)

## Built on by (most cited first, 13 of 13)

- [InkubaLM: A small language model for low-resource African languages](https://mlsanity.com/paper/2408.17024/) (2024)
- [Assessing SPARQL capabilities of Large Language Models](https://mlsanity.com/paper/2409.05925/) (2024)
- [GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases](https://mlsanity.com/paper/2504.05478/) (2025)
- [Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data](https://mlsanity.com/paper/2412.10654/) (2024)
- [Enhancing Text2Cypher with Schema Filtering](https://mlsanity.com/paper/2505.05118/) (2025)
- [SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection](https://mlsanity.com/paper/2507.13859/) (2025)
- [Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval](https://mlsanity.com/paper/2502.13369/) (2025)
- [LLM-KG-Bench 3.0: A Compass for SemanticTechnology Capabilities in the Ocean of LLMs](https://mlsanity.com/paper/2505.13098/) (2025)
- [Conversational Lexicography: Querying Lexicographic Data on Knowledge Graphs with SPARQL through Natural Language](https://mlsanity.com/paper/2505.19971/) (2025)
- [ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities](https://mlsanity.com/paper/2510.02200/) (2025)
- [QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines](https://mlsanity.com/paper/2505.07345/) (2025)
- [The benefits of query-based KGQA systems for complex and temporal questions in LLM era](https://mlsanity.com/paper/2507.11954/) (2025)
- [Personal Intelligence System UniLM: Hybrid On-Device Small Language Model and Server-Based Large Language Model for Malay Nusantara](https://mlsanity.com/paper/2410.06973/) (2024)

## Abstract (arXiv)

In this work we will show that language models with less than one billion parameters can be used to translate natural language to SPARQL queries after fine-tuning. Using three different datasets ranging from academic to real world, we identify prerequisites that the training data must fulfill in order for the training to be successful. The goal is to empower users of semantic web technology to use AI assistance with affordable commodity hardware, making them more resilient against external factors.