# Vietmeagent Eval

> This benchmark evaluates a model's ability to generate culturally accurate and coherent explanations for Vietnamese visual question answering. It probes both linguistic fluency and the model's capacity to ground visual evidence in domain-specific cultural knowledge through structured, stepwise reasoning. Use when the user wants to benchmark on Vietnamese VQA dataset, or asks about evaluating this task. Reports Cultural Accuracy.

- Skill: `qhjqhj00/vietmeagent-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/vietmeagent-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/vietmeagent-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/vietmeagent-eval

---


# vietmeagent-eval

> VietMEAgent: Culturally-Aware Few-Shot Multimodal Explanation for Vietnamese Visual Question Answering — Nguyen et al. (2025) (arXiv:2511.09058, 2025)

## What this evaluates

This benchmark evaluates a model's ability to generate culturally accurate and coherent explanations for Vietnamese visual question answering. It probes both linguistic fluency and the model's capacity to ground visual evidence in domain-specific cultural knowledge through structured, stepwise reasoning.

## Datasets

- **Vietnamese VQA dataset** — total ?; splits: (unstated)

## Metrics

- `BLEU-4` — range: [0, 1]
  - Standard 4-gram precision metric with a brevity penalty to penalize overly short generations. It measures surface-level lexical overlap between the predicted and reference rationales.
- `METEOR` — range: [0, 1]
  - Harmonic mean of unigram precision and recall, incorporating stemming and synonymy matching to better capture semantic equivalence than exact n-gram overlap.
- `ROUGE-L` — range: [0, 1]
  - F-score based on the longest common subsequence (LCS) between the predicted and reference texts, capturing sentence-level fluency and structural alignment.
- `Cultural Accuracy` **(primary)** — range: [0, 1]
  - Measures correctness against expert-validated cultural facts. It is computed as the proportion of generated explanations that correctly align with verified cultural knowledge entries for the depicted visual content.
- `Explanation Quality` — range: [0, 1]
  - Assesses the coherence and grounding of multimodal rationales. It evaluates whether the generated stepwise reasoning logically connects visual evidence to cultural semantics without hallucination or disjointed claims.

## Input / output format

**Input**: An image and a Vietnamese question requiring visual identification and cultural reasoning.

**Output**: A textual, stepwise explanation linking visual evidence to cultural semantics, followed by the final answer.

## Scoring recipe

```python
def compute_metrics(predictions, gold):
    bleu4 = compute_bleu(predictions, gold, n=4)
    meteor = compute_meteor(predictions, gold)
    rouge_l = compute_rouge_l(predictions, gold)
    
    # Domain-specific metrics rely on expert validation
    cultural_acc = sum(1 for p in predictions if is_culturally_valid(p)) / len(predictions)
    expl_quality = compute_coherence_grounding(predictions, gold)
    
    return {
        'BLEU-4': bleu4,
        'METEOR': meteor,
        'ROUGE-L': rouge_l,
        'Cultural Accuracy': cultural_acc,
        'Explanation Quality': expl_quality
    }
```

## Common pitfalls

- Cultural Accuracy depends entirely on expert-validated facts; models without access to the curated knowledge base will systematically underperform regardless of visual capability.
- Explanation Quality evaluates coherence and grounding, which can be highly subjective without a strict annotation rubric or inter-annotator agreement reporting.
- BLEU-4 may overestimate generation quality by rewarding fluent but culturally incorrect or hallucinated rationales.

## Evidence (verbatim from paper)

> We evaluate language quality with BLEU-4 *[papineni2002bleu]*, complement it with METEOR *[banerjee2005meteor]* and ROUGE-L *[lin2004rouge]*, and introduce two domain-oriented criteria: Cultural Accuracy, which measures correctness against expert-validated cultural facts, and Explanation Quality, which assesses the coherence and grounding of multimodal rationales.

## Citation

```bibtex
@misc{nguyen2025vietmeagent,
  title={VietMEAgent: Culturally-Aware Few-Shot Multimodal Explanation for Vietnamese Visual Question Answering},
  author={Nguyen et al. (2025)},
  year={2025},
  note={arXiv:2511.09058}
}
```

- arXiv: 2511.09058