SPARTA Benchmark

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SPARTA introduces a groundbreaking benchmark for tree-structured multi-hop question answering (QA) across text and tables, addressing the critical shortcomings of existing datasets like HybridQA and OTT-QA, which suffer from shallow reasoning, annotation errors, and limited scale. By constructing a unified reference fact database that merges source tables with grounding tables derived from unstructured passages, our end-to-end framework automates the generation of thousands of high-fidelity QA pairs—requiring only a quarter of the annotation effort—while incorporating advanced operations like aggregation, grouping, and deep nested predicates. Innovative techniques such as provenance-based refinement and realistic-structure enforcement ensure executable, semantically sound queries that mimic real-world complexity, spanning domains like NBA, movies, and medicine. On SPARTA, state-of-the-art models plummet by over 30 F1 points, exposing gaps in cross-modal reasoning and paving the way for more robust QA systems.

Dataset Structure

The dataset contains 3 configs across 3 domains (Movie, NBA, Medical), each with test and validation splits.

Domains

• Movie: Movie database with films, genres, ratings, and biographical text
• NBA: Basketball statistics with player info, teams, awards, and game narratives
• Medical: Healthcare data with appointments, billing, treatments, and patient records

Configs

Config	Splits	Description
workload_movie	test, validation	565 + 565 Movie domain queries
workload_nba	test, validation	565 + 565 NBA domain queries
workload_medical	test, validation	565 + 565 Medical domain queries

Splits

• test: question_id, question, table (3 fields only, answers withheld)
• validation: includes SQL query, answer, query metadata (13 fields)

Validation Split Fields

Field	Type	Description
question_id	string	Unique query ID ({domain}:{idx})
question	string	Natural language question
table	list[string]	Relevant table names
sql_query	string	SQL query
answer	list[string]	Ground truth answers
is_nested	bool	Whether query is nested
is_aggregated	bool	Whether query uses aggregation
height	int	Query nesting depth
breadth	dict	Breadth per level
max_breadth	int	Maximum breadth
type	string	Query type description
clause_usage	dict	SQL clause usage (WHERE, GROUP BY, etc.)
aggregate_usage	dict	Aggregate function usage (SUM, COUNT, etc.)

Usage

from datasets import load_dataset

# Load test split (questions only)
test_ds = load_dataset("pshlego/SPARTA", "workload_nba", split="test")

# Load validation split (with answers and metadata)
val_ds = load_dataset("pshlego/SPARTA", "workload_nba", split="validation")

License

CC BY-NC 4.0