--- license: mit pretty_name: "LEGEX Reproduction Bundle: Code, Pipeline, and Analysis Outputs" language: - en tags: - legal - benchmark - llm-evaluation - information-extraction - reproducibility viewer: false --- # LEGEX — Reproduction Bundle (release/icml2026) **LEGEX** is an expert-coded benchmark for civil-judgment review-table extraction: judgments from the highest civil courts of 19 jurisdictions, annotated by legal experts on a shared schema (monetary outcomes, cost allocation, party structure, industry classification), against which we evaluate two commercial review-table systems (Harvey, Legora) and two schema-constrained LLM pipelines (Gemini, ChatGPT). This bundle is the versioned code + data payload behind the paper *LEGEX: An Eight-Jurisdiction Benchmark for Legal Review-Table Extraction* (ICML 2026 Workshop on AI for Law); it contains an eight-jurisdiction core benchmark (≥100 judgments each, double-annotated on a 28–30-case overlap) and eleven preview jurisdictions. The release consists of three Hugging Face repositories, pinned to the `release/icml2026` branch: | Artifact | Contents | URL | |---|---|---| | `goldensets` | Expert gold labels, `data//goldenset_.jsonl` | | | `inference-results` | System predictions, `data//inference_{harvey,harvey_2,gemini,gpt,legora_1,legora_2}.jsonl`, plus the as-run per-column prompts of the commercial runs in `prompts/` | | | `code` | This bundle (package, scripts, shipped analysis outputs) | | ## Layout of this bundle ``` submission/ ├── README.md this file ├── pyproject.toml · LICENSE · .env.template ├── legex/ the LEGEX package, pruned to what the release uses: │ ├── scrapers/ one scraper per jurisdiction │ ├── fulltext/ full-text acquisition for the goldenset workbooks │ ├── prompts/ versioned system prompts (paper runs use v3) │ ├── inference.py legex-classify (LLM pipelines) │ ├── harvey.py legex-harvey-ingest (review-table export ingest) │ ├── evaluation/ tolerant cell comparator + scoring engine │ └── analysis/ aggregates, IAA, AAT rendering, report, plots ├── scripts/ paper-facing scripts (tables, figures, AAT adapter) ├── tests/ unit tests (uv run pytest -q) ├── data/analysis/ SHIPPED analysis outputs (see "Recompute" below) │ ├── per_country_per_column.csv · per_country.csv · per_column.csv │ ├── per_tradition.csv · per_language.csv │ ├── quant_results.tex paper Table tab:overall │ ├── paper_tables.tex tab:metrics-by-jurisdiction / tab:metrics-by-field │ ├── tables/ diversity.tex, currency_frequencies.tex, ... │ ├── figures/ isic_frequencies.pdf, legal_subject_wordcloud.png │ ├── iaa/ ANALYSIS.md, pairwise_agreement.csv, kappa_audit.csv, │ │ alt_test_pooled.csv, alt_test_reference_*.csv │ ├── hallucinations/ hallucination_review_ch.csv (hand-classified error case study) │ └── quality/ by_country.csv, by_variable.csv ├── goldensets/ 1:1 copy of legexbenchmark/goldensets (dataset card + data) ├── inference-results/ 1:1 copy of legexbenchmark/inference-results ├── convert_goldenset_to_jsonl.py XLSX gold workbooks -> goldenset_.jsonl ├── build_inference_jsonl.py cleaned working JSONL -> inference_.jsonl ├── docs/Jurisdictions.md per-jurisdiction research catalogue └── HUGGINGFACE_UPLOAD.md how this bundle maps onto the three HF repos ``` ## Reproduce the paper tables and figures All commands are run **from this directory** (the bundle root). Requires [uv](https://docs.astral.sh/uv/) and Python ≥ 3.11. ```bash uv sync ``` **Table `tab:overall` (headline metrics).** Re-render from the shipped per-cell aggregate and check it is byte-identical to the shipped table: ```bash uv run legex-quant-results --input data/analysis/per_country_per_column.csv --out /tmp/quant_results.tex diff /tmp/quant_results.tex data/analysis/quant_results.tex ``` **Tables `tab:metrics-by-jurisdiction` and `tab:metrics-by-field`.** ```bash uv run python scripts/paper_tables.py > /tmp/paper_tables.tex diff /tmp/paper_tables.tex data/analysis/paper_tables.tex ``` **IAA / kappa / Alternative Annotator Test report** (`ANALYSIS.md`, the source of the paper's `tab:iaa` and AAT numbers). The report is rendered purely from the shipped CSVs: ```bash uv run legex-analysis-report --iaa-dir data/analysis/iaa --analysis-dir data/analysis --out /tmp/ANALYSIS.md diff /tmp/ANALYSIS.md data/analysis/iaa/ANALYSIS.md ``` **One-shot reproduction.** `scripts/reproduce_paper.sh` regenerates every artifact under `data/analysis/` — IAA CSVs, scoring aggregates, all tables, the diversity/frequency figures, the hallucination shares, and `ANALYSIS.md` — from the published bundles in `goldensets/data/` and `inference-results/data/`. The run is byte-stable, so a clean `git status data/analysis` afterwards is the verification that the shipped numbers reproduce: ```bash bash scripts/reproduce_paper.sh git status data/analysis ``` There are minimal known deviations from the submitted PDF. The gold labels are normalised during export to JSONL (controlled-token case, thousands separators, Excel dates → ISO), and the paper's tables were computed from the raw annotator workbooks before that normalisation, where, for example, a gold cell coded `Nonpecuniary` counted against a system answering the schema literal `nonpecuniary`. Re-scoring on the published gold therefore yields slightly higher values in a few places than the PDF: Philippines recall (+7–9 pp for every system), Spain (≈ +1 pp), `tab:overall` within ±0.2 pp, and the money/ratio agreement rows of `tab:iaa` (+1–2 pp). The κ values, pair counts, denominators, the hallucination case study, and all remaining numbers match the PDF. The shipped outputs under `data/analysis/` are the published-data regeneration. The deviations could not be resolved due to time constraints and are made transparent here. ## Unpublished raw data — scope The expert-annotated XLSX workbooks (`data//Goldenset_*_final*.xlsx`) behind the goldensets are **not published**: Microsoft Office embeds author metadata, and the re-annotation assignment structure could identify individual annotators. The gold *labels* are published in full as `goldensets/data//goldenset_.jsonl` (primary + anonymized secondary annotator rows), and every analysis in this bundle runs from those published files — the workbooks are not needed for reproduction. **Alternative Annotator Test (AAT).** The AAT (Calderon, Reichart & Dror, ACL 2025, [arXiv:2501.10970](https://arxiv.org/abs/2501.10970)) is run with the authors' original implementation via the adapter `scripts/alt_test_reference.py`, which extracts `alt_test()` from the upstream notebook at runtime and feeds it LEGEX data from the published bundles: ```bash git clone https://github.com/nitaytech/AltTest /tmp/AltTest ALTTEST_DIR=/tmp/AltTest bash scripts/reproduce_paper.sh # or: uv run python scripts/alt_test_reference.py --alttest /tmp/AltTest \ --gold-dir goldensets/data --inference-dir inference-results/data \ [--countries ge,sg,tw] [--epsilon 0.2] [--out data/analysis/iaa] [--per-field] ``` Its outputs (`alt_test_pooled.csv`, `alt_test_reference_*.csv`) are shipped under `data/analysis/iaa/` and rendered into `ANALYSIS.md` by `legex-analysis-report`. ## Scraping The scrapers that built the corpus are included (`legex/scrapers/`, one file per jurisdiction; see `docs/Jurisdictions.md` for the per-country catalogue). ```bash cp .env.template .env # then fill in what you need uv run legex-run ``` `legex-run` executes the idempotent pipeline (scrape → filter/sample → fill goldenset → dist) for every registered jurisdiction and skips countries whose access is not set up or whose output already exists. Some sources need manual prerequisites (all keys go into `.env`, see [.env.template](.env.template)): - **Hugging Face** (`au`, `ch`, `de`, `us`): a read token as `HF_TOKEN`; `de` and `us` additionally require accepting the gated-dataset terms of `openlegaldata/court-decisions-germany` and `free-law/Caselaw_Access_Project`. - **France**: register at , subscribe to the Judilibre API, set `JUDILIBRE_CLIENT_ID` / `JUDILIBRE_CLIENT_SECRET`. - **New Zealand**: copy the WAF cookie from a justice.govt.nz session into `NZ_WAF_COOKIE`. - **India**: `legex-india-extract` pulls selected PDFs from the AWS Open Data TAR archives (see `legex/scrapers/in_.py`). Model inference over the scraped full texts (the paper runs use prompt v3): ```bash uv run legex-classify --model --full_text --prompt_version v3 ``` Tests: `uv run pytest -q`. ## Citation ```bibtex @inproceedings{legex2026, title = {{LEGEX}: An Eight-Jurisdiction Benchmark for Legal Review-Table Extraction}, author = {K{\"o}nig, Adrian and others}, booktitle = {ICML 2026 Workshop on AI for Law (AI4Law)}, year = {2026}, url = {https://huggingface.co/legexbenchmark} } ``` ## License MIT. See [LICENSE](LICENSE).