Dataset Card for FinMultiHopQA

FinMultiHopQA is a KG-grounded benchmark of 15,600 qualitative and quantitative multi-hop questions over SEC 10-K filings. Every question is built from a typed path in FinReflectKG, a provenance-linked financial knowledge graph, and ships with the exact evidence chunks (filing, page, chunk) for each hop.

Dataset Details

Dataset Description

Curated by: Domyn

Language(s): English

License: CC-BY-NC-4.0

FinMultiHopQA tests whether models can combine evidence that is scattered across pages, filings, fiscal years, and companies. Questions require 2 or 3 reasoning hops and cover three document regimes: intra-document, inter-year (same company, different fiscal years), and cross-company. Answers are grounded only in the released evidence chunks, so no external knowledge is needed.

Dataset Sources

Base knowledge graph: FinReflectKG | Paper: FinReflectKG: Agentic Construction and Evaluation of Financial Knowledge Graphs

Benchmark paper: FinMultiHopQA: Financial QA Benchmark for Reasoning with Knowledge Graph Evidence

Key Features

• 15,600 QA pairs: 7,800 qualitative and 7,800 quantitative; 6,000 2-hop and 9,600 3-hop
• Three document regimes: intra-document, inter-year, and cross-company
• Two 3-hop motif families: connector chains (type1) and three-anchor branches (type2)
• Full provenance: every hop carries its source filing, page, chunk ID, and chunk text
• Coverage: 96 companies, 267 10-K filings, fiscal years 2022–2024, 11 GICS sectors
• Quality-screened: every pair scored by an LLM validator (≥ 45/50) plus hard rejection rules
• Expert-validated: a two-SME quality audit on a 360-pair random sample

Quick Start

from datasets import load_dataset

ds = load_dataset("domyn/FinMultiHopQA", split="test")  # 15,600 questions

# Filter by question type, hop count, topology, or motif
quant = ds.filter(lambda r: r["question_type"] == "quantitative")
three_hop = ds.filter(lambda r: r["hop_count"] == 3)
inter_year = ds.filter(lambda r: r["topology"] == "cross_year" and r["motif"] == "chain")

row = ds[0]
print(row["question"])
for hop in row["hops"]:
    print(hop["hop"], hop["relationship"], hop["source_file"], hop["page_id"])

Dataset Structure

Files and splits

The full benchmark is a single test split (15,600 rows). Use the question_type, hop_count, topology, and motif columns to select subsets.

Statistics

Question type	Topology	2-hop	3-hop	Total
Qualitative	Cross-company	1,250	2,000	3,250
Qualitative	Inter-year	1,000	1,600	2,600
Qualitative	Intra-document	750	1,200	1,950
Quantitative	Cross-company	1,250	2,000	3,250
Quantitative	Inter-year	1,000	1,600	2,600
Quantitative	Intra-document	750	1,200	1,950
Total		6,000	9,600	15,600

Motifs: 10,000 chain (all 2-hop + 3-hop type1) and 5,600 branch (3-hop type2).

Sector coverage (GICS): Information Technology 2,321 · Industrials 2,106 · Health Care 2,019 · Financials 1,952 · Consumer Staples 1,828 · Consumer Discretionary 1,611 · Communication Services 1,518 · Energy 853 · Utilities 853 · Real Estate 371 · Materials 168

Partitions

Each partition keeps the top-N candidates by quality_score (descending), then source question ID (ascending).

Partition	Questions	Sampling	Quality score (min / max / mean)
qualitative_2hop_cross_company	1,250	top_quality_score	46 / 49 / 46.78
qualitative_2hop_cross_year	1,000	top_quality_score	46 / 49 / 46.64
qualitative_2hop_intra_doc	750	top_quality_score	46 / 50 / 47.14
qualitative_3hop_cross_company_type1	600	top_quality_score	47 / 48 / 47.03
qualitative_3hop_cross_company_type2	1,400	top_quality_score	47 / 48 / 47.15
qualitative_3hop_cross_year_type1	800	top_quality_score	47 / 48 / 47.02
qualitative_3hop_cross_year_type2	800	top_quality_score	46 / 48 / 47.06
qualitative_3hop_intra_doc_type1	600	top_quality_score	47 / 48 / 47.03
qualitative_3hop_intra_doc_type2	600	top_quality_score	46 / 48 / 46.72
quantitative_2hop_cross_company	1,250	top_quality_score	47 / 49 / 47.13
quantitative_2hop_cross_year	1,000	top_quality_score	47 / 49 / 47.08
quantitative_2hop_intra_doc	750	top_quality_score	47 / 50 / 47.27
quantitative_3hop_cross_company_type1	600	top_quality_score	46 / 48 / 46.97
quantitative_3hop_cross_company_type2	1,400	top_quality_score	47 / 48 / 47.09
quantitative_3hop_cross_year_type1	800	top_quality_score	47 / 48 / 47.02
quantitative_3hop_cross_year_type2	800	top_quality_score	47 / 49 / 47.22
quantitative_3hop_intra_doc_type1	600	top_quality_score	47 / 48 / 47.03
quantitative_3hop_intra_doc_type2	600	top_quality_score	46 / 48 / 46.82

Data Fields

Field	Type	Description
id	string	Unique key: <partition>-<row index>
question	string	The multi-hop question
answer	string	Reference answer grounded in the evidence chunks
reasoning_steps	list[string]	Step-by-step reasoning chain behind the reference answer
question_type	string	qualitative or quantitative
hop_count	int	2 or 3
topology	string	intra_doc, cross_year, or cross_company (from the partition; use this for topology analysis)
motif	string	chain (2-hop and 3-hop type1) or branch (3-hop type2)
pattern	string	Typed KG path, e.g. ORG -[Invests_In]-> GPE <-[Operates_In]- ORG
document_relationship	string	intra_document, inter_document_same_company, or inter_document_cross_company
sector	string	GICS sector
sub_industry	string	GICS sub-industry (null when not resolved)
difficulty	string	IDF-based heuristic: easy / medium / hard
idf_score	float	Connector IDF score (higher = more specific connector)
quality_score	int	LLM-validator rubric total (0–50)
nodes	list[struct]	KG path nodes: {role, id, name, type, year, idf_score}
hops	list[struct]	Evidence per hop: {hop, relationship, source_file, page_id, chunk_id, chunk_text}

Node roles: 2-hop paths use start, intermediate, end; 3-hop chains use start, node_2, node_3, end; 3-hop branches use start, connector (the shared node the anchors link to), end. For every row, hops[i].source_file gives the filing behind hop i.

Example

{
  "id": "qualitative_3hop_cross_company_type2-00000",
  "question": "How do Boeing (BA), Emerson (EMR), and Honeywell (HON) each approach the strategic asset allocation for their defined benefit pension plans, ...?",
  "answer": "Boeing focuses on reducing asset volatility relative to liabilities, ...",
  "reasoning_steps": ["Hop 1: BA → Asset Allocation: ...", "Hop 2: EMR → Asset Allocation: ...", "Hop 3: HON → Asset Allocation: ..."],
  "question_type": "qualitative",
  "hop_count": 3,
  "topology": "cross_company",
  "motif": "branch",
  "pattern": "ORG -[Monitors]-> ACCOUNTING_POLICY <-[Discloses]- ORG <-[Discloses]- ORG",
  "sector": "Industrials",
  "difficulty": "medium",
  "quality_score": 48,
  "nodes": [
    {"role": "start", "id": "BA", "name": "BA", "type": "ORG", "year": 2024, "idf_score": null},
    {"role": "connector", "id": "Asset_Allocation", "name": "Asset Allocation", "type": "ACCOUNTING_POLICY", "year": null, "idf_score": 4.01},
    {"role": "end", "id": null, "name": "EMR", "type": "ORG", "year": null, "idf_score": null}
  ],
  "hops": [
    {"hop": 1, "relationship": "Monitors", "source_file": "BA_10k_2024.pdf", "page_id": "page_99", "chunk_id": "chunk_1", "chunk_text": "..."},
    {"hop": 2, "relationship": "Discloses", "source_file": "EMR_10k_2024.pdf", "page_id": "page_63", "chunk_id": "chunk_5", "chunk_text": "..."},
    {"hop": 3, "relationship": "Discloses", "source_file": "HON_10k_2024.pdf", "page_id": "page_107", "chunk_id": "chunk_3", "chunk_text": "..."}
  ]
}

Dataset Creation

Motif families

Questions are instantiated from typed KG motifs, each under three evidence regimes with explicit provenance constraints:

• 2-hop bridge (cross_company, cross_year, intra_doc): cross-company uses two distinct organizations with matched fiscal years; inter-year uses one organization across different years; intra-document uses one document but distinct pages/chunks (or distinct facts in a shared chunk).
• 3-hop connector chain (type1): connector pairs are chosen by IDF ranking within GICS strata, which suppresses generic connectors; the linking relation must share provenance with the anchor edges.
• 3-hop multi-branch (type2): three organization anchors (or one organization across three years) each linked independently to one shared connector node.

Generation and filtering

The pipeline generates a full corpus of 36,668 QA pairs; this benchmark is the filtered subset of 15,600. Filtering preserves the topology and sector balance of the full corpus.

Quality control

Every generated pair is scored by an LLM validator on five dimensions (0–10 each, max 50): multi-chunk dependency, financial context, answer grounding, causal-connection grounding, and natural analyst quality. Pairs below 45/50 are regenerated. Independent of the score, a pair is discarded if it:

• mentions page or section structure;
• is answerable from one evidence fact without combining the required hops;
• needs external knowledge not present in the evidence;
• asserts a fabricated causal link between companies that only share a generic term.

The rubric total is released as quality_score on every row.

Human (SME) validation

Two finance SMEs independently rated a fixed-seed, topology-stratified random sample of 360 QA pairs on four criteria. A separate SME judge-calibration study scored model answers on a distinct 360-pair panel (1,080 assessments).

Uses

• Evaluating LLM multi-hop reasoning over financial filings
• Comparing evidence-delivery strategies (KG-linked chunks vs. page windows vs. retrieval)
• Studying how performance changes with hop count, topology, and motif

The benchmark paper evaluates six evidence protocols: KG-linked provenance (E1), page windows (E2), page windows with distractors (E3), relevance-sorted descending (E4) and ascending (E5), and semantic retrieval (E6).

Known Issues

• difficulty is an IDF heuristic (~80% hard), not a calibrated difficulty measure.

Citation

If you use this dataset, please cite:

@article{arun2026finmultihopqa,
  title={FinMultiHopQA: Financial QA Benchmark for Reasoning with Knowledge Graph Evidence},
  author={Arun, Abhinav and Harsh, Reetu Raj and Sarmah, Bhaskarjit and Pasquali, Stefano},
  journal={arXiv preprint},
  year={2026}
}

FinMultiHopQA is built on FinReflectKG:

@article{arun2025finreflectkg,
  title={FinReflectKG: Agentic Construction and Evaluation of Financial Knowledge Graphs},
  author={Arun, Abhinav and Dimino, Fabrizio and Agarwal, Tejas Prakash and Sarmah, Bhaskarjit and Pasquali, Stefano},
  journal={arXiv preprint arXiv:2508.17906},
  year={2025},
  url={https://arxiv.org/pdf/2508.17906}
}