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Corp_Finance Long-Context Reasoning HuggingFace Dataset.parquet

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README Corp_Finance Long-Context Reasoning HuggingFace.md

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+---
+pretty_name: "Corp_Finance Long-Context Reasoning — Expert-Authored Credit Agreement Benchmark (Showcase Sample)"
+language:
+  - en
+license: cc-by-4.0
+task_categories:
+  - question-answering
+tags:
+  - finance
+  - leveraged-finance
+  - credit-agreements
+  - legal
+  - expert-annotation
+  - benchmark
+  - reasoning
+  - long-context
+  - human-in-the-loop
+size_categories:
+  - n<1K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train-00000-of-00001.parquet
+---
+
+# Corp_Finance Long-Context Reasoning — Expert-Authored Credit Agreement Benchmark (Showcase Sample)
+
+A five-record public sample from **Corp_Finance Long-Context Reasoning**, a subject-matter-expert benchmarking dataset built by [Human Edge](https://humanedgetech.ai) for evaluating frontier model reasoning over leveraged finance and syndicated credit documentation.
+
+Every question, reasoning trace, and answer in this dataset was authored by a practising finance professional and independently reviewed by 2-3 others. Nothing here is model-generated.
+
+> **This is a curated showcase, not a training corpus.** It exists to make the authoring and review methodology inspectable. See [Scope and Selection](#scope-and-selection).
+
+---
+
+## Why this dataset exists
+
+Frontier models are fluent on credit documentation and unreliable on it. They resolve defined terms to generic market meanings instead of the agreement's own, stop at the first hop of a definition chain, drop qualifying conditions when summarising, and make arithmetic errors on covenant calculations. These failures are hard to detect precisely because the output reads well.
+
+The difficulty is structural. A credit agreement runs to hundreds of pages, and the information needed to answer a single question is rarely in one place: a covenant threshold in one section depends on a defined term in another, which carves out an exception defined in a third. Answering correctly means holding the whole chain at once — which is why this dataset is built around long-context reasoning rather than retrieval.
+
+Catching those failures requires evaluators who already know what the right answer looks like. This dataset is built around that constraint: expert authorship, expert review, and a reasoning trace that has to be defensible step by step against a cited section.
+
+---
+
+## Scope and Selection
+
+| | |
+|---|---|
+| Records in this sample | **5** |
+| Records in the full corpus | 33 |
+| Source documents | 2 |
+| Fields per record | 11 |
+| Format | Parquet (Snappy) |
+| Split | `train` (single split; no train/test partition — this is an evaluation set) |
+
+The five records were selected to (a) maximise reviewer-assigned Overall Quality and (b) span the widest possible range of question types and both source documents. Where a task identifier existed in both documents, the higher-scoring version was taken.
+
+### Records included
+
+| Question ID | Document | Question type | Difficulty (R1 / R2) | Overall Quality (R1 / R2) |
+|---|---|---|---|---|
+| Task #3 - Q&A #7 | Doc #1 — Leveraged Finance | Summarization | 4 / 5 | **5 / 5** |
+| Task #3 - Q&A #2 | Doc #1 — Leveraged Finance | Market Opinion | 4 / 5 | **5 / 5** |
+| Task #2 - Q&A #6 | Doc #2 — Syndicated Credit | Industry Jargon | 5 / 4 | 5 / 4 |
+| Task #1 - Q&A #2 | Doc #1 — Leveraged Finance | Numeric Reasoning | 4 / 4 | 4 / 4 |
+| Task #3 - Q&A #6 | Doc #2 — Syndicated Credit | Multi-Section Reference | 3 / 4 | 4 / 4 |
+
+Mean Overall Quality across the ten independent reviewer ratings in this sample: **4.5 / 5**. All five records passed all four review dimensions under both reviews; this file carries the citation-correctness verdicts, with the other three dimensions omitted as uniformly `Pass` across the sample.
+
+> `Question Type` is a field of the full corpus but is **not** a column in this sample file. The types above are stated here for orientation.
+
+---
+
+## Source documents
+
+Both source agreements are real, publicly filed credit agreements, available in full from the SEC's EDGAR system. No part of this benchmark depends on a private document.
+
+| | `Doc #1` | `Doc #2` |
+|---|---|---|
+| **Instrument type** | Leveraged Finance | Syndicated Credit |
+| **Borrower** | Novelis Holdings Inc. | Seagate HDD Cayman |
+| **Parent** | Novelis Inc. | Seagate Technology Holdings plc |
+| **Administrative Agent** | Citibank, N.A. | The Bank of Nova Scotia |
+| **Dated** | 11 March 2025 | 30 January 2025 |
+| **Facility** | $1.25bn Term Loan B | $1.3bn revolving credit facility |
+| **Filing** | EX-10.9 to Novelis Inc. Form 10-K, filed 12 May 2025 | EX-10.1 to Seagate Technology Holdings plc Form 8-K, filed 3 February 2025 |
+| **Full text** | [SEC EDGAR](https://www.sec.gov/Archives/edgar/data/1304280/000130428025000023/novelis-2025tlbcreditagree.htm) | [SEC EDGAR](https://www.sec.gov/Archives/edgar/data/1137789/000113778925000023/seagate-creditagreementedg.htm) |
+
+The two documents were chosen to contrast: a secured leveraged term loan with an extensive covenant package and multi-jurisdiction guarantee structure, against a large investment-grade-style syndicated revolver whose collateral obligations are themselves rating-triggered. Both run to several hundred pages.
+
+**Every record is independently verifiable.** Because the agreements are public, any reader can open the cited section and check the reasoning trace against the actual contractual language — the claim and its evidence are both inspectable. Doc #1 records cite sections including 5.15 (Designation of Subsidiaries), 6.04 (Investments, Loans and Advances) and 6.07 (Dividends); Doc #2 records cite sections including 1.01 (Defined Terms) and 2.17 (Defaulting Lenders). Note that section numbering differs between the two agreements, so a section reference is only meaningful alongside its `Document ID`.
+
+Human Edge built this benchmark on public filings by design, so that the work can be shared, audited, and reproduced without an NDA. The full corpus draws on these same two agreements.
+---
+
+## Question type taxonomy
+
+The full corpus is stratified across six categories, each targeting a distinct, documented model failure mode. Five of the six are represented in this sample.
+
+**Summarization** — synthesis across multiple clauses into an accurate summary. Targets the tendency to omit qualifying conditions or conflate related-but-distinct provisions. Requires genuine synthesis, not verbatim reproduction of one paragraph.
+
+**Multi-Section Reference** — tracing a definition chain across two or more sections. Targets failure at the second or third hop: stopping at the first definition found, or hallucinating the contents of the next section. Chains must not be short-circuitable.
+
+**Industry Jargon** — resolving a defined term to *this agreement's* contractual meaning and identifying how it departs from standard market usage. Targets the default to generic definitions without consulting the document.
+
+**Numeric Reasoning** — multi-step financial calculation using values drawn from the document. The highest failure rate of any category in the project: frontier models make arithmetic errors, skip steps, or pull values from the wrong section.
+
+**Market Opinion** — combining document evidence with professional judgment to evaluate a provision against market norms. Targets the two opposite evasions: restating the document without evaluating it, or asserting a market view without grounding it in the text.
+
+**Basic Extraction** *(not in this sample)* — locating an explicitly stated fact. A deliberate floor-coverage baseline against which higher-complexity performance is measured. Citation and reasoning requirements are identical to every other type.
+
+---
+
+## How each record was produced
+
+Authoring, machine pre-screening, and human review are separate stages performed by different parties. No stage grades its own work, and no rating published in this file was produced by the person who wrote the record.
+
+| Stage | Performed by | Produces |
+|---|---|---|
+| 1. Authoring | **Task Author** (finance SME) | `Question`, `Reasoning Steps`, `Answer` |
+| 2. Automated pre-screen | Internal LLM | Clarity and reasoning-completeness gating *(not included in this sample)* |
+| 3. Independent review ×2–3 | **Task Reviewers** (finance SMEs) | Eight `P/F` verdicts across four dimensions *(two included here)*, `Difficulty (R1/R2)`, `Overall Quality (R1/R2)` |
+
+Reviewers work independently and are not shown each other's verdicts, which is why both ratings are published separately rather than averaged.
+
+Records that fail review are revised and re-reviewed before inclusion in the corpus. Every record in this sample cleared all four dimensions under at least two independent reviews.
+
+---
+
+## Field reference
+
+### Identifiers
+
+| Field | Type | Description |
+|---|---|---|
+| `Question ID` | string | Task and Q&A index, e.g. `Task #3 - Q&A #7`. **Unique only in combination with `Document ID`** — the same identifier appears against both source documents in the full corpus. |
+| `Document ID` | string | Source agreement: `Doc #1` (Leveraged Finance) or `Doc #2` (Syndicated Credit). |
+
+### Content
+
+| Field | Type | Populated by | Description |
+|---|---|---|---|
+| `Question` | string | Task Author | The stimulus posed to the model. |
+| `Reasoning Steps` | string | Task Author | The logical path, intermediate computations, and document lookups leading to the answer. Each step pairs a step description with the citation supporting it. Flattened to numbered readable text in this file — see [Transformations](#transformations-from-source). |
+| `Answer` | string | Task Author | The response that satisfies the question. |
+
+### Reviewer ratings
+
+`R1` and `R2` denote the first and second independent reviewer throughout this dataset. All four columns are populated by Task Reviewers.
+
+| Field | Type | Description |
+|---|---|---|
+| `Difficulty (R1)` | int32 | First reviewer's complexity rating, 1–5. |
+| `Difficulty (R2)` | int32 | Second reviewer's complexity rating, 1–5. |
+| `Overall Quality (R1)` | int32 | First reviewer's overall quality rating, 1–5. |
+| `Overall Quality (R2)` | int32 | Second reviewer's overall quality rating, 1–5. |
+
+Difficulty scale:
+
+| | |
+|---|---|
+| **1** | Direct extraction from a single, easily located clause. No interpretation. |
+| **2** | Explicit information requiring careful reading, or located in a less-prominent section. |
+| **3** | Synthesis across multiple sub-clauses, simple calculation, or basic cross-referencing. |
+| **4** | Multi-step reasoning, integration across separate sections, or professional judgment against market standards. |
+| **5** | Complex chains (4+ hops), advanced calculations with term resolution, or nuanced market evaluation requiring expert interpretation. |
+
+### Review verdicts
+
+Two columns, both `Pass` / `Fail`, populated by Task Reviewers.
+
+| Field | Description |
+|---|---|
+| `Citation Correctness P/F (R1)` | First reviewer: do the cited sections genuinely support the answer? |
+| `Citation Correctness P/F (R2)` | Second reviewer: same question, assessed independently. |
+
+Citation correctness is scored separately from factual accuracy by design. An answer can be entirely correct while pointing at the wrong section, and that combination is the failure most likely to survive casual review — which is exactly why it is the dimension retained here.
+
+Review covers four dimensions in total. The other three — factual accuracy, answer completeness, and reasoning trajectory — are recorded in the full corpus and are `Pass` for every record in this sample under both reviews.
+
+## Fields present in the full corpus but not in this sample
+
+Withheld to keep the sample readable and to publish only independently-assigned ratings — not because of any concern about the values:
+
+- `Question Type` — the taxonomy label (stated above per record instead)
+- Author self-assessed difficulty — the task author's own complexity rating of their own work. Excluded so that every rating published here is an independent one.
+- Aggregate quality score — the mean of the two reviewer quality ratings. Excluded in favour of publishing both underlying ratings, which carry strictly more information.
+- `Factual Accuracy P/F`, `Answer Completeness P/F`, `Reasoning Trajectory P/F` (R1 and R2) — three of the four review dimensions, uniformly `Pass` across these five records
+- Automated pre-screen layer — clarity and reasoning-completeness scores with their written justifications, plus the overall pass flag
+- Pre-revision submissions — the originally submitted question, reasoning steps, and answer, which allow before/after inspection of what review actually changed
+- Pipeline bookkeeping — review counts and inclusion flags
+
+The full corpus, including the pre-revision fields, is available under NDA.
+
+---
+
+## Intended use and limitations
+
+**Appropriate for:** inspecting expert-authored evaluation methodology; understanding how reasoning traces and citation requirements are specified for financial documentation; assessing whether this annotation standard fits an evaluation programme.
+
+**Not appropriate for:** benchmarking or scoring a model. Five records support no statistical claim whatsoever. Fine-tuning.
+
+**Known limitations**
+
+- Sample size is 5 (of 33 in total). It is illustrative by construction.
+- Selection is deliberately biased toward high-scoring records. The full corpus contains records that failed review dimensions on first pass; this sample contains none.
+- Two source documents, both US-market credit agreements. No coverage of other instrument types, jurisdictions, or governing law.
+- `Basic Extraction` is absent, so the difficulty range is skewed upward relative to the full corpus.
+- All content is English.
+
+---
+
+## License & Citation
+
+This sample is released under the **[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)** license. You may share and adapt it, including for commercial purposes, provided you give appropriate credit.
+
+The license covers Human Edge's contribution — the questions, reasoning traces, answers, and review ratings — and does not extend to the underlying credit agreements themselves, which are the parties' own documents and are reproduced here only by reference to their public SEC filings. It does not extend to the remainder of the corpus.
+
+```bibtex
+@misc{humanedge_corpfinance_longcontext_2026,
+  title  = {Corp_Finance Long-Context Reasoning: Expert-Authored Credit
+            Agreement Benchmark (Showcase Sample)},
+  author = {Human Edge Technology},
+  year   = {2026}
+}
+```
+
+## About Human Edge
+
+Human Edge Technology builds expert human data for AI development — SME-based evaluation, RLHF, and domain benchmarking across finance, legal, and healthcare. Our model is straightforward: the people authoring and reviewing the data are practitioners in the domain being evaluated.
+
+If you are building or evaluating models against specialist documentation and need data at this standard, we should talk.
+
+**[humanedgetech.ai](https://humanedgetech.ai)**