| --- |
| 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/<cc>/goldenset_<cc>.jsonl` | <https://huggingface.co/datasets/legexbenchmark/goldensets/tree/release/icml2026> | |
| | `inference-results` | System predictions, `data/<cc>/inference_{harvey,harvey_2,gemini,gpt,legora_1,legora_2}.jsonl`, plus the as-run per-column prompts of the commercial runs in `prompts/` | <https://huggingface.co/datasets/legexbenchmark/inference-results/tree/release/icml2026> | |
| | `code` | This bundle (package, scripts, shipped analysis outputs) | <https://huggingface.co/datasets/legexbenchmark/code/tree/release/icml2026> | |
|
|
| ## 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_<cc>.jsonl |
| ├── build_inference_jsonl.py cleaned working JSONL -> inference_<model>.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/<cc>/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/<cc>/goldenset_<cc>.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 <https://piste.gouv.fr>, 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 <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). |
|
|
