# QBR: A Question-Bank-Based Approach to Fine-Grained Legal Knowledge Retrieval for the General Public

arXiv: https://arxiv.org/abs/2505.04883 · HTML: https://mlsanity.com/paper/2505.04883/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 14 of 14)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling](https://mlsanity.com/paper/2104.06967/) (2021)
- [SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval](https://mlsanity.com/paper/2009.13013/) (2020)
- [Contrastive Fine-tuning Improves Robustness for Neural Rankers](https://mlsanity.com/paper/2105.12932/) (2021)
- [Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval](https://mlsanity.com/paper/2308.00762/) (2023)
- [Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive Learning](https://mlsanity.com/paper/2403.10939/) (2024)
- [Assessing the Readability of Policy Documents on the Digital Single Market of the European Union](https://mlsanity.com/paper/2102.11625/) (2021)
- [Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model](https://mlsanity.com/paper/2505.04132/) (2025)

## Built on by (most cited first, 2 of 2)

- [A Context-Aware Dual-Metric Framework for Confidence Estimation in Large Language Models](https://mlsanity.com/paper/2508.00600/) (2025)
- [Generative Chinese Statute Retrieval](https://mlsanity.com/paper/2607.11109/) (2026)

## Abstract (arXiv)

Retrieval of legal knowledge by the general public is a challenging problem due to the technicality of the professional knowledge and the lack of fundamental understanding by laypersons on the subject. Traditional information retrieval techniques assume that users are capable of formulating succinct and precise queries for effective document retrieval. In practice, however, the wide gap between the highly technical contents and untrained users makes legal knowledge retrieval very difficult. We propose a methodology, called QBR, which employs a Questions Bank (QB) as an effective medium for bridging the knowledge gap. We show how the QB is used to derive training samples to enhance the embedding of knowledge units within documents, which leads to effective fine-grained knowledge retrieval. We discuss and evaluate through experiments various advantages of QBR over traditional methods. These include more accurate, efficient, and explainable document retrieval, better comprehension of retrieval results, and highly effective fine-grained knowledge retrieval. We also present some case studies and show that QBR achieves social impact by assisting citizens to resolve everyday legal concerns.