README.md

---
license: cc-by-4.0
language:
- en
pretty_name: 'Human Edge Legal Reasoning Evaluation — Showcase Sample'
size_categories:
- n<1K
task_categories:
- text-generation
- question-answering
tags:
- legal
- law
- legal-reasoning
- evaluation
- rubric
- rubrics
- reward-model
- rlhf
- rlvr
- expert-annotation
- benchmark
configs:
- config_name: default
  data_files:
  - split: train
    path: Legal_Reasoning_Task_Examples.parquet
dataset_info:
  features:
  - name: task_id
    dtype: int32
  - name: area
    dtype: string
  - name: prompt
    dtype: string
  - name: golden_answer
    dtype: string
  - name: associated_rubrics
    dtype: int32
  - name: rubrics
    list:
    - name: rubric_category
      dtype: string
    - name: score_option
      dtype: int32
    - name: criterion
      dtype: string
    - name: justification
      dtype: string
  - name: task_details
    dtype: string
  - name: reference_materials
    dtype: string
  - name: realistic_explanation
    dtype: string
  - name: difficulty_explanation
    dtype: string
  - name: peer_review_1_overall_quality_label
    dtype: string
  - name: peer_review_1_overall_quality_description
    dtype: string
  - name: peer_review_1_representativeness_label
    dtype: string
  - name: peer_review_1_difficulty_label
    dtype: string
  - name: peer_review_1_difficulty_description
    dtype: string
  - name: peer_review_1_task_specification_quality_label
    dtype: string
  - name: peer_review_2_overall_quality_label
    dtype: string
  - name: peer_review_2_overall_quality_description
    dtype: string
  - name: peer_review_2_representativeness_label
    dtype: string
  - name: peer_review_2_difficulty_label
    dtype: string
  - name: peer_review_2_difficulty_description
    dtype: string
  - name: peer_review_2_task_specification_quality_label
    dtype: string
  splits:
  - name: train
    num_examples: 6
---

# Human Edge — Legal Reasoning Evaluation (Showcase Sample)

A public 6-task sample from a rubric-based legal reasoning evaluation dataset built by
**Human Edge** ([humanedgetech.ai](https://humanedgetech.ai)). Each task is authored and reviewed by
practicing senior lawyers and is designed to produce a **verifiable, per-criterion reward signal**
for post-training and evaluation of frontier language models on high-complexity legal work.

The sample contains **one task per legal subdomain**, drawn from a larger internal corpus of
50 expert-authored tasks.

- **Curated by:** Human Edge
- **Language:** English
- **License:** CC BY 4.0
- **Repository:** [HumanEdgeAI/LegalReasoning](https://huggingface.co/datasets/HumanEdgeAI/LegalReasoning)

## Why this dataset exists

Most legal evaluations reduce to multiple-choice recall or bar-exam-style questions. Real legal work
does not look like that: it is open-ended, multi-jurisdictional, and judged on reasoning quality,
appropriate hedging, and whether the sources actually say what the answer claims they say.

This dataset takes the opposite approach. Every task is a realistic instruction from a practicing
lawyer's desk, paired with an expert-authored reference answer and a **fine-grained rubric of
weighted, binary criteria** totalling exactly 100 points per task (29–43 criteria in this sample).
The rubric — not the reference answer — is the object of
evaluation. That makes scoring reproducible across graders and directly convertible into
per-dimension reward signals.

## Task architecture

Each row is a triple-component unit:

| Component | Field | What it is |
|---|---|---|
| 1. Legal reasoning prompt | `prompt` | A realistic scenario requiring multi-step reasoning and professional judgment, with role, jurisdiction, and deliverable format specified |
| 2. Golden answer | `golden_answer` | An expert-authored reference analysis (17k–38k characters in this sample) |
| 3. Scoring rubric | `rubrics` | 29–43 categorized, weighted, binary criteria per task, positive weights totalling 100 points, each with the author's justification |

## Rubric design

Criteria are binary and weighted. Positive weights reward required content; negative weights
penalize specific failure modes (hallucinated authority, overconfident advice, missing caveats).

**Every task's rubric is calibrated to the same point budget.** Positive criteria total exactly
**100 points**, allocated across the three positive categories in a fixed split:

| Category | Sub-category | Point budget | Criteria | What it tests |
|---|---|---|---|---|
| **Substance** | | **65** | 108 | |
| | Explicit requirements | | 27 | Did the answer do what was literally asked? |
| | Implicit requirements | | 27 | Did it surface what a competent practitioner would raise unprompted? |
| | Legal correctness | | 22 | Is the law stated accurately? |
| | Reasoning quality | | 32 | Is the analytical path sound, not just the conclusion? |
| **Sources & References** | — | **20** | 34 | Are cited authorities real, relevant, and correctly characterized? |
| **Structure & Style** | — | **15** | 32 | Does it read like professional work product? |
| **Negative Criteria** | — | *penalties* | 25 | Penalties for specific, anticipated failure modes |
| **Total** | | **100** | **199** | |

The point budget holds for all six tasks individually, not just in aggregate — so a model's raw
score is already a percentage, and scores are directly comparable across tasks and subdomains
despite differing criteria counts. Negative criteria sit outside the 100 points and subtract from
the earned total, so a score can fall below zero.

The criteria counts vary by task (29–43) because experts allocated the fixed budget at whatever
granularity the material demanded — a task needing many small checks uses more `+1` criteria, one
turning on a few decisive points uses `+5`s.

The `rubric_category` field stores each of these as a single literal string. Filter on these values
exactly:

`Substance Rubric - Explicit requirements`, `Substance Rubric - Implicit requirements`,
`Substance Rubric - Legal correctness`, `Substance Rubric - Reasoning quality`,
`Sources and References Rubric`, `Structure & Style Rubric`, `Negative Rubric`

Weight distribution across the 199 criteria in this sample:
`+5` (71), `+3` (71), `+1` (32), `-1` (2), `-3` (7), `-5` (16).

## Contents

Six tasks, one per subdomain:

| `task_id` | `area` | Rubric criteria | Peer review overall quality (R1 / R2) |
|---|---|---|---|
| 18 | Employment & Labor | 34 | 3 / 4 |
| 40 | Intellectual Property (IP) | 43 | 4 / 4 |
| 68 | Commercial Litigation | 34 | 4 / 5 |
| 82 | Regulatory & Compliance | 29 | 4 / 5 |
| 93 | Corporate & M&A | 30 | 4 / 4 |
| 100 | Contract Law | 29 | 4 / 5 |

Jurisdictions are US and UK (England and Wales). `task_id` values are the original corpus
identifiers and are intentionally non-contiguous.

## Who wrote and reviewed these tasks

Tasks were authored by senior legal practitioners recruited against a hard credential bar:
8+ years in practice, a Master's or PhD in law, and a background in AmLaw 100 / Magic Circle firms,
senior courts, government, or in-house at large enterprises.

The contributing cohort averaged **~18.5 years of practice**, split roughly 75% US / 25% UK,
with education spanning T14 US law schools and leading UK and European universities. Identity was
verified for every contributor before any project interaction.

Domain expertise alone does not make a calibrated evaluator, so every contributor completed a
mandatory training program (~2.3 hours average) on stress-testing model outputs, applying rubrics
consistently, and documenting rationale.

## Quality assurance

Three stages, applied to every task in the source corpus:

1. **Automated checks.** Every task passes programmatic validation before it reaches a reviewer.
   Prompts are checked for scenario framing, role specification, deliverable format, and
   jurisdictional context. Rubrics are checked for binary format compliance and a minimum criterion
   count. A model-graded pass confirms each rubric is consistent with its golden answer.
2. **Double-blind expert peer review.** Two independent senior practitioners review each task,
   rating overall quality, difficulty, representativeness, and task specification quality, each with
   written rationale. Reviews drive iterative revision rather than a simple accept/reject: the author
   revises against reviewer comments, and the version shipped here reflects those revisions.
3. **Difficulty validation.** Tasks are stress-tested against frontier models. A task is admitted
   only if strong models still fail a meaningful share of its rubric criteria — tasks that models
   solve comfortably carry no training signal and are rejected.

Tasks in the source corpus averaged roughly eleven hours of expert effort each, counting authoring,
peer review, and revision. Reviewer ratings clustered tightly, indicating consensus among
practitioners rather than averaged-out disagreement.

## Field reference

### Task content

| Field | Type | Description |
|---|---|---|
| `task_id` | int32 | Original corpus identifier |
| `area` | string | Legal subdomain |
| `prompt` | string | The legal reasoning prompt given to the model |
| `golden_answer` | string | Expert-authored reference answer |
| `associated_rubrics` | int32 | Number of rubric criteria; always equals `len(rubrics)` |
| `rubrics` | list&lt;struct&gt; | Nested rubric criteria (see below) |
| `task_details` | string | Author's note on why the task is hard or interesting |
| `reference_materials` | string | Source authorities relied on; free-text, sometimes URLs, sometimes citations |

### `rubrics` struct

| Field | Type | Description |
|---|---|---|
| `rubric_category` | string | One of the seven literal values listed above |
| `score_option` | int32 | Weight: `+5`, `+3`, `+1`, `-1`, `-3`, `-5` |
| `criterion` | string | Binary question applied to the model's answer |
| `justification` | string | Author's rationale for why this criterion matters; empty for 5 of 199 criteria (see Limitations) |

Text has been normalized: non-breaking spaces, stray tabs and other exotic whitespace introduced
by the authoring tools have been folded to plain spaces. Newlines in `golden_answer` are preserved.

### Author commentary

The task author's own notes on realism and difficulty. Their numeric self-ratings are deliberately
**not** included — an author's rating of their own task is not independent evidence, so the only
ratings in this dataset come from the two peer reviewers.

| Field | Type | Description |
|---|---|---|
| `realistic_explanation` | string | Why the scenario reflects real practice |
| `difficulty_explanation` | string | What makes the task hard, and where models are expected to fail |

### Peer review

Two independent reviewers per task, both prefixed: `peer_review_1_` is the first-round review
(conducted on the first draft version), `peer_review_2_` the second (conducted on the second draft
version). The final version shipped here was produced after revising comments from the second
reviewer. Reviewer identities are not published.

The `*_label` fields carry the rating exactly as the reviewer selected it — score and wording
together, e.g. `4 - Above Standards`. To get a numeric value, split on the first `-`.

| Field suffix | Type | Description |
|---|---|---|
| `overall_quality_label` | string | Rating 1–5, e.g. `4 - Above Standards` |
| `overall_quality_description` | string | Reviewer's written rationale for the quality rating |
| `representativeness_label` | string | Rating 1–5 — how typical this task is of real practice, e.g. `5 - Core Activity` |
| `difficulty_label` | string | Rating 1–5, e.g. `3 - Moderate` |
| `difficulty_description` | string | Reviewer's written rationale for the difficulty rating |
| `task_specification_quality_label` | string | Categorical: `Well-Specified`, `Overspecified`, or `Underspecified` |

Rating scales, as presented to reviewers:

- **Overall quality:** `1 - Far Below Standards`, `2 - Below Standards`, `3 - Meets Minimum Standards`, `4 - Above Standards`, `5 - Exemplary`
- **Representativeness:** `1 - Rarely Encountered`, `2 - Uncommon`, `3 - Somewhat Typical`, `4 - Typical`, `5 - Core Activity`
- **Difficulty:** `1 - Trivial` → `5 - Very Hard` (`3 - Moderate`, `4 - Hard`)
- **Task specification quality:** `Underspecified`, `Well-Specified`, `Overspecified`

## Personal and sensitive information

The dataset contains no personal data. Scenarios are built on fictional parties and hypothetical facts; the only real names are the litigants in cited case law, which is public record. Author and reviewer identities are not published.

## Intended uses

- Evaluating LLM performance on open-ended, high-complexity legal reasoning
- Rubric-as-reward research: RLVR, reward model training, process supervision
- LLM-as-judge calibration — the rubrics give a judge concrete, verifiable criteria instead of a
  vague quality prompt
- Studying legal-domain failure modes, especially source and citation reliability
- A reference template for anyone constructing expert-authored rubric evaluations in other
  professional domains

**Out of scope:** legal advice of any kind. The golden answers are evaluation artifacts written
against hypothetical facts, not guidance on any real matter, and must not be relied on as such.

## Limitations

- **Six tasks.** This is a methodology sample, not a benchmark. Do not report aggregate scores over
  six tasks as a model capability claim.
- **Scoring requires a judge.** Criteria are binary but not mechanically checkable; they need a
  competent grader, human or model. Judge choice will shift absolute scores.
- **US and UK only.** Nothing here generalizes to civil law systems, EU-level practice, or other
  common law jurisdictions without revalidation.
- **Contamination risk.** Published openly, these tasks may enter future training corpora. Treat
  results on this sample as indicative once models trained after its publication are involved.
- **Reviewer identities are withheld.** Cohort-level credentials are described above; individual
  names and affiliations are not published.
- **Five criteria carry no justification.** Four in task 40 and one in task 100 have an empty
  `justification`; the authors did not record one. The criteria themselves are complete, scored, and
  counted in the point budget — only the explanatory note is absent. Every other field is populated
  in every row.

## License

Released under the **Creative Commons Attribution 4.0 International** license ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)).

You are free to share and adapt this dataset for any purpose, including commercially, provided you give appropriate credit to Human Edge, link to the license, and indicate whether changes were made.

The license covers Human Edge's contribution — the prompts, golden answers, rubrics, and review ratings. It does not grant rights in the third-party statutes, regulations, and court opinions cited within the tasks; those remain governed by their own terms. It does not extend to the remainder of the corpus.

## Citation

If you use this dataset, please cite it:

**BibTeX:**

```bibtex
@misc{humanedgeai2026legalreasoning,
  title     = {Human Edge Legal Reasoning Evaluation: Showcase Sample},
  author    = {{Human Edge}},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/HumanEdgeAI/LegalReasoning}
}
```

**APA:**

Human Edge. (2026). *Human Edge Legal Reasoning Evaluation: Showcase Sample* [Data set]. Hugging Face. https://huggingface.co/datasets/HumanEdgeAI/LegalReasoning

## About Human Edge

Human Edge builds expert human data for AI development — SME-based evaluation, benchmarking, and reinforcement learning from expert feedback in domains where correctness requires professional judgment: finance, legal, healthcare, and tax.

This dataset is a sample of the pilot phase of a larger program. The production methodology scales the cohort, the review pipeline, and the volume well past what is shown here.

**To discuss an evaluation or benchmarking engagement: [humanedgetech.ai](https://humanedgetech.ai)**