# Improving LLMs with a knowledge from databases

arXiv: https://arxiv.org/abs/2506.05560 · HTML: https://mlsanity.com/paper/2506.05560/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data](https://mlsanity.com/paper/2402.17644/) (2024)
- [EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning](https://mlsanity.com/paper/2405.07938/) (2024)
- [GRS-QA -- Graph Reasoning-Structured Question Answering Dataset](https://mlsanity.com/paper/2411.00369/) (2024)
- [ARULESPY: Exploring Association Rules and Frequent Itemsets in Python](https://mlsanity.com/paper/2305.15263/) (2023)

## Abstract (arXiv)

Large language models (LLMs) are achieving significant progress almost every moment now. Many advanced techniques have been introduced and widely accepted, like retrieval-augmentation generation (RAG), agents, and tools. Tools can query the database to answer questions from structured data files or perform groupings or other statistics. This unlocks huge opportunities, such as it can answer any question, but also poses threats, such as safety, because there is no control over the commands that are created. We would like to discuss whether we can create a new method that improves answers based on dataset/database via some interpretable ML methods, namely enhanced association rules. The advantage would be if the method can be also used in some safe technique like RAG. Association rules have a sound history. Since the introduction of CN2 and aproiri, many enhancements have been made. In parallel, enhanced association rules have been introduced and evolved over the last 40 years. The general problem is typically that there are too many rules. There are some techniques for handling it, but when LLM emerged, it turned out to be the best use case for the RAG technique for LLMs. We proposed a method that generates a ruleset based on defined knowledge patterns, then converts rules into text form via a rule-to-text converter, and includes the result as an RAG into LLM. We compared this method with ChatGPT (even with using agents) and we have discovered a significant improvement in answering questions based on the dataset. We have also tried several strategies how much rules to generate. We found this improvement interesting. Moreover, it can also be improved in many ways as future work, like incorporating other patterns, the use of rule mining as an agent, and many others.