Fingtruthqa Eval

---
name: fingtruthqa-eval
description: Evaluates AI models' ability to assess the quality of financial information disclosure in Chinese. It probes four capabilities: identifying relevant questions, determining question relevance, evaluating answer readability, and measuring answer relevance to the question. Use when the user wants to benchmark on FinTruthQA, or asks about evaluating this task. Reports Accuracy, Precision, Recall, F1-score, Micro/Macro/Weighted F1, Quadratic Weighted Kappa (QWK).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2406.12009
  bibtex_key: xu2024fingtruthqa
  confidence: high
---

# fingtruthqa-eval

> FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information Disclosure — Xu et al. (2024) (arXiv:2406.12009, 2024)

## What this evaluates

Evaluates AI models' ability to assess the quality of financial information disclosure in Chinese. It probes four capabilities: identifying relevant questions, determining question relevance, evaluating answer readability, and measuring answer relevance to the question.

## Datasets

- **FinTruthQA** — total 6000; splits: test (-1)

## Metrics

- `Accuracy, Precision, Recall, F1-score, Micro/Macro/Weighted F1, Quadratic Weighted Kappa (QWK)` **(primary)** — range: percent
  - Standard classification metrics. Accuracy, Precision, Recall, and F1-score for binary tasks (question identification and relevance). Micro, Macro, and Weighted F1-scores plus Quadratic Weighted Kappa (QWK) for ordinal/multi-class tasks (answer readability and relevance). QWK penalizes misclassifications based on their distance from the true label.

## Input / output format

**Input**: For question identification and relevance: raw Chinese question text. For answer readability and relevance: concatenated question and answer text, separated by [SEP] token, prefixed with [CLS] token.

**Output**: Classification label: binary (relevant/irrelevant or identified/not) for tasks 1-2; ordinal/multi-class label (e.g., 1-3 for readability/relevance levels) for tasks 3-4.

## Scoring recipe

```python
def compute_metrics(preds, golds, task_type):
    if task_type == 'binary':
        acc = accuracy_score(golds, preds)
        prec, rec, f1, _ = precision_recall_fscore_support(golds, preds, average='binary')
        return acc, prec, rec, f1
    else:
        f1_micro = f1_score(golds, preds, average='micro')
        f1_macro = f1_score(golds, preds, average='macro')
        f1_weighted = f1_score(golds, preds, average='weighted')
        qwk = quadratic_weighted_kappa(golds, preds)
        return f1_micro, f1_macro, f1_weighted, qwk
```

## Common pitfalls

- Highly imbalanced label distributions (e.g., 910 positive vs. 7 negative samples for question relevance) can cause models to trivially predict all positives to achieve high recall.
- LLMs trained primarily on English corpora struggle with Chinese financial jargon, causing Chain-of-Thought prompting to degrade performance rather than improve it.
- Continued pre-training on domain-specific data only benefits models already aligned with the financial domain (e.g., FinBERT), while others may suffer from representation mismatch.

## Evidence (verbatim from paper)

> For question identification and question relevance, which are binary classification tasks, we used accuracy, precision, recall, and F1-score as evaluation metrics. For answer readability and answer relevance, which are multi-class classification tasks, we calculated both micro and macro F1-scores, along with the Quadratic Weighted Kappa (QWK), which is well-suited for ordinal classification by penalizing misclassifications based on their distance from the true label.

## Citation

```bibtex
@misc{xu2024fingtruthqa,
  title={FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information Disclosure},
  author={Xu et al. (2024)},
  year={2024},
  note={arXiv:2406.12009}
}
```

- arXiv: 2406.12009