README.md

---
license: cc-by-nc-4.0
language:
- en
tags:
- synthetic
- finance
- question-answering
- instruction-tuning
- cfa
- adversarial
- llm-evaluation
- multi-turn
pretty_name: "Financial Q&A Dataset: Investment-Domain Questions with Tiered Answers, Conversations & Adversarial Variants (Sample)"
size_categories:
- n<1K
configs:
- config_name: questions
  default: true
  data_files: fqa_001_questions.csv
- config_name: answers
  data_files: fqa_001_answers.csv
- config_name: qa_pairs
  data_files: fqa_001_qa_pairs.csv
- config_name: entities
  data_files: fqa_001_entities.csv
- config_name: topic_taxonomy
  data_files: fqa_001_topic_taxonomy.csv
- config_name: personas
  data_files: fqa_001_personas.csv
- config_name: conversations
  data_files: fqa_001_conversations.csv
- config_name: adversarial_pairs
  data_files: fqa_001_adversarial_pairs.csv
---

# FQA-001 — Financial Q&A Dataset: Investment-Domain Questions with Tiered Answers, Conversations & Adversarial Variants (Sample)

Synthetic **investment-domain question-and-answer corpus** with controlled difficulty, topic distribution, tiered answers, multi-turn conversations, and adversarial variants. Questions span 12 CFA curriculum domains (Equities, Fixed Income, Derivatives, Portfolio Management, Economics, Alternative Investments, Corporate Finance, Risk Management, Regulation & Ethics, Quantitative Methods, Real Assets, Behavioral Finance) across 8 structural formats (definition, calculation, comparison, scenario, what-if, error-spotting, best-of-N, regulatory-applicability).

This is a **500-question sample** of the full FQA-001 product (50,000 questions). It is **synthetic** — generated by a deterministic, benchmark-anchored engine. It contains **no real financial advice, securities data, or PII**. Tickers, entities, and prices are fictional.

> **Not investment advice.** This dataset is for ML training/evaluation, instruction-tuning, retrieval/QA benchmarking, and research only. It must not be used as a source of financial, investment, legal, or regulatory advice.

## Unit of observation

The unit is the **question**. Each question has 4 answers (1 gold + 1 silver + 2 plausible-wrong distractors). Tables key on `question_id` / `answer_id` / `entity_id`. The sample is entirely within the engine's **train split** (chronological cutoff at `question_id` 40000); the `train_test_split` column is retained for schema fidelity.

## Calibration anchors

Sample-level observed values (seed 42, 500 questions):

| Metric | Observed | Target | Anchor |
|---|---|---|---|
| CFA L1 topic abs deviation | 0.12 | ≤0.22 | CFA Level-I topic-area weights (12 domains) |
| Bloom mean level | 2.90 | 2.7–3.2 | Bloom's taxonomy CFA skew (~2.95) |
| Numerical-question fraction | ~0.40 | 0.20–0.55 | calculation/scenario template share |
| Adversarial attack-mix abs deviation | 0.16 | ≤0.30 | TruthfulQA + AdvGLUE + OWASP LLM Top 10 |
| Adversarial refusal fraction | ~0.59 | 0.40–0.65 | pii_probe + jailbreak + prompt_injection |
| Conversation resolution rate | ~0.72 | 0.60–0.84 | Accenture financial-chatbot benchmark |
| Entity top-10% reference share | high | ≥0.18 (floor) | Zipf(s=1.2) entity reuse (Reuters corpus) |
| Gold-per-question violations | 0 | =0 (floor) | exactly one gold answer |
| Answers-per-question violations | 0 | =0 (floor) | exactly four answers |
| Gold error-type violations | 0 | =0 (floor) | gold carries error_type='none' |
| QA-pair integrity violations | 0 | =0 (floor) | valid gold id + 3 distractors |

Validation: **Grade A+ (10.00/10)** across all six canonical seeds (42, 7, 123, 2024, 99, 1). All eight data CSVs are byte-for-byte deterministic per seed.

## Tables

Eight relational CSVs + manifest:

- **`fqa_001_questions.csv`** — 500 questions × 22 cols: text, topic L1/L2, Bloom level, difficulty, format type, persona, entity refs, numerical/multi-hop/temporal/negation flags, token/sentence counts, train/test split.
- **`fqa_001_answers.csv`** — 2,000 answers × 8 cols: answer text, quality tier (gold/silver/plausible_wrong), is_gold, error type, error severity, token count.
- **`fqa_001_qa_pairs.csv`** — 500 rows × 9 cols: gold answer id, distractor id JSON array, context snippet, topic, Bloom, difficulty.
- **`fqa_001_entities.csv`** — 500 entities × 10 cols: synthetic ticker/name, sector, asset class, OU price anchor, credit rating.
- **`fqa_001_topic_taxonomy.csv`** — 129 rows × 7 cols: L1/L2 hierarchy with CFA weights and query-frequency indices.
- **`fqa_001_personas.csv`** — 10 personas × 7 cols: knowledge level, risk tolerance, query style.
- **`fqa_001_conversations.csv`** — 200 conversations × 8 cols: multi-turn dialogues (Markov topic drift), resolution flag, turn sequence JSON.
- **`fqa_001_adversarial_pairs.csv`** — 200 rows × 11 cols: attack type, adversarial text, expected-refusal flag, jailbreak/injection/hallucination scores.
- **`fqa_001_manifest.json`** — metadata, row counts, seed, parameters, split definition.

## Loading

```python
import pandas as pd, json
questions = pd.read_csv("fqa_001_questions.csv")
answers   = pd.read_csv("fqa_001_answers.csv")
qa_pairs  = pd.read_csv("fqa_001_qa_pairs.csv")

# Assemble a gold-answer training pair
row = qa_pairs.iloc[0]
gold = answers.loc[answers.answer_id == row.gold_answer_id, "answer_text"].iloc[0]
q    = questions.loc[questions.question_id == row.question_id, "question_text"].iloc[0]
print(q, "->", gold)
```

```python
from datasets import load_dataset
questions = load_dataset("xpertsystems/fqa-001-sample", "questions")
```

## Use cases

- Instruction-tuning / SFT on tiered (gold/silver/distractor) financial QA.
- Reward-model / preference data (gold > silver > plausible_wrong).
- Retrieval-QA and multiple-choice (best-of-N) benchmarking.
- LLM safety / adversarial-robustness evaluation (prompt injection, jailbreak, PII probe, hallucination bait, out-of-scope).
- Multi-turn dialogue and topic-drift modeling.
- Difficulty-calibrated (Bloom) curriculum learning and topic-coverage analysis.

## Limitations (honestly disclosed)

- **Synthetic, template-generated text; not investment advice.** Questions and answers are produced from deterministic template pools with sampled numerics and fictional entities. Answer prose is structurally coherent but not authored or fact-checked by finance professionals; gold answers are "correct by construction" within the template logic, not verified against real markets.
- **Taxonomy count note.** The engine's header comment describes "200" L2 subtopics, but the actual taxonomy defines **129** subtopics across 12 L1 domains. The manifest row count is computed correctly from the taxonomy (129), so the data is internally consistent; only the header comment overstates the figure.
- **Sample is train-split only.** The engine's chronological train/test split cuts at `question_id` 40000, so a 500-question sample is entirely "train". The column is retained for schema fidelity; the full product spans both splits.
- **Adversarial mix is seed-stable.** Per-record sub-RNGs (answers, conversations, adversarial pairs) are seeded from their record id, so the adversarial attack-type *distribution* is consistent across seeds (well-calibrated to target weights). Question and entity content still varies by seed. This is a determinism design choice, not a defect.
- **Marginal calibration, not full joint fidelity.** Topic/Bloom/adversarial/error-tier distributions and the structural integrity guarantees are anchored; higher-order correlations (e.g. topic × difficulty × persona joint structure) beyond what the templates encode are not independently validated.
- **Small-sample variance.** At 500 questions the 12-domain topic and 5-category adversarial distributions carry multinomial sampling noise; scorecard bands accommodate this and the structural integrity floors are weighted to dominate.

## Commercial / full version

| | Sample (this) | Full (commercial) |
|---|---|---|
| Questions | 500 | 50,000 |
| Answers | 2,000 | 200,000 |
| Entities | 500 | 5,000 |
| Conversations | 200 | 5,000 |
| Adversarial pairs | 200 | 5,000 |
| Topic taxonomy | 129 subtopics | 129 subtopics |
| Train/test split | train only | train (≤39999) + test (≥40000) |
| Bloom skew / topic regime | CFA / balanced | cfa\|uniform\|advanced × balanced\|equity_heavy\|macro_heavy |
| Formats | CSV + manifest | CSV / JSON + manifest |
| Seeds / reproducibility | 6 canonical | Unlimited |
| License | CC-BY-NC-4.0 | Commercial |
| Support | — | SLA, custom topic regimes, adversarial intensity tuning |

Contact **pradeep@xpertsystems.ai** · https://xpertsystems.ai

## Citation

```bibtex
@dataset{xpertsystems_fqa001_2026,
  title  = {FQA-001: Synthetic Financial Q&A Dataset --- Investment-Domain Questions
            with Tiered Answers, Multi-Turn Conversations & Adversarial Variants (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  publisher = {Hugging Face},
  note   = {Synthetic data. Not investment advice. Fictional entities. Calibration
            anchors: CFA Institute Level-I curriculum topic-area weights; Bloom's
            taxonomy item-difficulty skew; FinQA (Chen et al. 2021) error analysis;
            TruthfulQA + AdvGLUE + OWASP LLM Top 10 adversarial taxonomy; Reuters
            corpus Zipf entity reuse; Accenture financial-chatbot resolution benchmark.},
  url    = {https://huggingface.co/datasets/xpertsystems/fqa-001-sample}
}
```