SearchFireSafety (ACL 2026)

Official dataset repository for the ACL 2026 paper: Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA

• Preprint: https://arxiv.org/abs/2604.06173
• Paper: https://aclanthology.org/2026.acl-long.2112/

Overview

SearchFireSafety is a benchmark for statute-centric legal QA in the Korean fire-safety domain. The dataset is designed to evaluate:

• Structure-aware retrieval over citation-linked legal documents
• Multi-hop reasoning across delegated statutory provisions
• Safe abstention behavior under partial/incomplete context

Repository Scope

This repository is organized as a dataset archive. The core release is under data/:

• data/legal_docs.jsonl: legal corpus (article-level units) + citation links
• data/realworld_qa.jsonl: real-world expert QA pairs
• data/multihop_qa_mcq.jsonl: synthetic multi-hop MCQ for safety evaluation

Installation

The dataset statistics script and default graph builder use only the Python standard library. To run dense retrieval evaluation, install the minimal runtime dependencies:

pip install -r requirements.txt

If you need a CUDA-specific PyTorch build, install PyTorch for your platform first, then install the requirements above.

Dataset Statistics

Recompute these statistics with:

python scripts/compute_dataset_stats.py

Category	Statistic	Number
Legal Documents	Total documents	4,468
Legal Documents	Avg. document length	477.9 characters
Legal Documents	Avg. words per document	103.2
Legal Documents	Avg. related documents	1.8
Real-World Expert QA	Total pairs	876
Real-World Expert QA	Avg. question length	90.7 characters
Real-World Expert QA	Avg. answer length	278.1 characters
Real-World Expert QA	Avg. relevant docs per question	1.5
Multi-Hop QA (MCQ)	Total pairs	3,395
Multi-Hop QA (MCQ)	Avg. question length	51.1 characters
Multi-Hop QA (MCQ)	Relevant docs per question	2.0

File Formats

1) legal_docs.jsonl

Article-level legal corpus entries.

Field	Type	Description
doc_id	int	Unique document unit ID
semantic_id	string	Human-readable legal identifier
collection_name	string	Parent legal collection
law_level	string	Legal hierarchy level (e.g., Act, Decree, Rule)
law_name	string	Law title
chapter	string	Article/appendix label
chapter_description	string	Article heading
text	string	Legal text
related_doc_ids	int[]	Citation/delegation-linked doc_id list

Notes:

• 1,728 rows contain at least one outgoing related_doc_ids entry.
• 2,740 rows have an empty related_doc_ids list.
• related_doc_ids defines graph edges used for structure-aware retrieval.

2) realworld_qa.jsonl

Real-world public petition questions with official NFA answers.

Field	Type	Description
question_id	int	Question ID
question	string	User question
answer	string	Official expert answer
related_doc_ids	int[]	Supporting legal document IDs
semantic_ids	string[]	Supporting semantic identifiers

3) multihop_qa_mcq.jsonl

Synthetic multiple-choice QA designed to test strict multi-hop dependency.

Field	Type	Description
question_id	int	Question ID
related_doc_ids	int[]	Source document IDs used to construct the question
related_semantic_ids	string[]	Semantic identifiers for source docs
question	string	MCQ question
option_1 ~ option_5	string	Five answer options
answer_full	int (1-5)	Correct option under full context
answer_partial	int (1-5)	Correct option under partial context

Notes:

• For all 3,395 rows, answer_partial = 5 ("Cannot be answered with the given information").
• This setup explicitly evaluates safe abstention under missing evidence.

Structure-Aware Reranking Evaluation

This repository also includes lightweight scripts for evaluating Structure-Aware Reranking (SAR) on the real-world QA split.

SAR is evaluated as a strict top-100 reranker in this release. The dense retriever first retrieves the top 100 documents. SAR then keeps the same candidate set and reranks it using explicit document links from legal_docs.jsonl. The real-world QA labels are used only for evaluation, not for graph construction.

Build the Explicit Graph

Directed graph:

python scripts/build_legal_explicit_graph.py \
--output legal_explicit_graph.pkl

Undirected graph:

python scripts/build_legal_explicit_graph.py \
--undirected \
--output legal_explicit_graph_undirected.pkl

The directed graph contains:

• 4,468 legal document nodes
• 1,728 documents with at least one related_doc_ids entry
• 1,650 referenced target documents
• 1,725 graph nodes with outgoing edges
• 8,114 directed adjacency entries

The undirected graph contains:

• 4,468 legal document nodes
• 1,728 documents with at least one related_doc_ids entry
• 1,650 referenced target documents
• 2,276 graph nodes with edges
• 15,262 undirected adjacency entries

SAR Pseudo Algorithm

Input:
q: user question
D: legal document corpus
G: explicit legal graph, where G[source_doc_id] = [neighbor_doc_id, ...]
K = 100: dense candidate size
M = 15: SAR voting seed size
F = 5: frozen dense anchor size
beta = 0.30: structural bonus weight
1. Dense retrieval
Encode q and all documents in D.
Compute dense score S_dense(d) for each document d.
Let C be the top-K documents by S_dense.
Let Seeds be the top-M documents in C.
2. Structural voting
Initialize bonus B(d) = 0 for each document d in C.
For each seed s in Seeds:
neighbors = G[s]
seed_penalty = log(|neighbors| + 1) if |neighbors| > 1 else 1
vote = S_dense(s) / seed_penalty
For each neighbor n in neighbors:
If n is not in C, skip it in strict top-100 mode.
target_penalty = log(indegree(n) + 1) if indegree(n) > 1 else 1
B(n) = B(n) + vote / target_penalty
3. Residual fusion
For each candidate d in C:
If d is one of the top-F dense anchors:
keep d above the remaining candidates.
Else:
S_SAR(d) = S_dense(d) + beta * B(d) * (1 - S_dense(d))
4. Return candidates C sorted by S_SAR.

Run Retrieval Evaluation

python scripts/eval_realworld_graph_retrieval.py \
--graph-pkl legal_explicit_graph.pkl \
--qa-file data/realworld_qa.jsonl \
--model-name BAAI/bge-m3 \
--save-json results_legal_graph_sar_top100.json

For the undirected graph, replace --graph-pkl and --save-json:

python scripts/eval_realworld_graph_retrieval.py \
--graph-pkl legal_explicit_graph_undirected.pkl \
--qa-file data/realworld_qa.jsonl \
--model-name BAAI/bge-m3 \
--save-json results_legal_graph_sar_top100_undirected.json

Results

The table below reports retrieval performance on data/realworld_qa.jsonl using BAAI/bge-m3. Values are percentages. Rocchio is evaluated as a candidate-only top-100 reranker with top_k=15, alpha=0.90, and beta=0.10.

Method	R@10	nDCG@10	MRR@10	R@20	nDCG@20	MRR@20	R@50	nDCG@50	MRR@50
Baseline	53.14	37.35	35.26	61.46	39.61	35.83	72.66	42.07	36.16
Rocchio	52.87	37.45	35.37	61.87	39.92	36.00	72.77	42.31	36.32
SAR (Directed)	55.45	38.22	35.59	63.62	40.47	36.15	73.46	42.65	36.45
SAR (Undirected)	54.18	37.77	35.37	62.97	40.18	35.97	73.40	42.48	36.30

Citation

If you use this dataset, please cite the ACL 2026 paper.

@inproceedings{chae-etal-2026-evaluating,
title = "Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal {QA}",
author = "Chae, Kyubyung and Yeom, Jewon and Park, Jeongjae and Bae, Seunghyun and Jang, Ijun and Jin, Hyunbin and Jang, Jinkwan and Kim, Taesup",
editor = "Liakata, Maria and Moreira, Viviane P. and Zhang, Jiajun and Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2112/",
doi = "10.18653/v1/2026.acl-long.2112",
pages = "45553--45573",
ISBN = "979-8-89176-390-6"
}

Contact

For questions about the dataset release, please open an issue in this repository.