leaderboard-harvesting

Research

Systematically collect performance data from platforms and papers

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Leaderboard Harvesting

Purpose

Harvest structured performance data from leaderboard platforms (Papers With Code, benchmark-specific sites), survey papers, and official benchmark repositories. Produces deduplicated, provenance-tracked score collections.

Stages

Stage 1: Platform Scan

Identify and scrape all relevant leaderboard sources:

Papers With Code task pages
Benchmark-specific leaderboards (e.g., GLUE, ImageNet, WMT)
GitHub benchmark repositories
Survey papers with comprehensive comparison tables

Yield: List of leaderboard URLs + initial method counts per source.

Stage 2: Paper Extraction

For methods not covered by leaderboards, extract scores directly from papers:

Original method papers (primary source)
Ablation studies and follow-up papers
Reproduction studies and benchmarking papers

Yield: Raw score tuples with paper provenance.

Stage 3: Cross-Validation

Compare scores across sources for the same method-dataset-metric triple:

Flag discrepancies > 1 standard deviation
Prefer primary sources when conflicts exist
Note which scores come from official vs. unofficial implementations

Yield: Validated score set with confidence annotations.

Stage 4: Dedup and Merge

Consolidate all sources into a single canonical dataset:

Resolve method name aliases
Merge duplicate entries with provenance tracking
Assign confidence levels based on source agreement

Yield: Unified performance dataset ready for analysis.

Minimum Yield

Metric	Floor
Leaderboard sources checked	3
Methods with scores	15
Cross-validated score pairs	10
Deduplication conflicts resolved	5

SOPs Used

method-discovery (for finding methods on leaderboards)
score-extraction (for paper-based extraction)
discrepancy-identification (for cross-validation)

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
discrepancy-identification	Compare same-method scores across sources, flag significant deviations
method-discovery	Identify all relevant methods via literature, leaderboards, citation chains
score-extraction	Extract (Task, Dataset, Metric, Score, Conditions) tuples from a paper