Mm Upd Eval

---
name: mm-upd-eval
description: Evaluates Large Multimodal Models' ability to detect unsolvable visual-question problems and appropriately refuse to answer. It specifically probes three failure modes: absent answers, incompatible answer sets, and image-question mismatches. Use when the user wants to benchmark on MM-UPD Bench, or asks about evaluating this task. Reports Dual accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2403.20331
  bibtex_key: [REDACTED]
  confidence: high
---

# mm-upd-eval

> Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models — Miyai et al. (2024) (arXiv:2403.20331, 2024)

## What this evaluates

Evaluates Large Multimodal Models' ability to detect unsolvable visual-question problems and appropriately refuse to answer. It specifically probes three failure modes: absent answers, incompatible answer sets, and image-question mismatches.

## Datasets

- **MM-UPD Bench** — total ?; splits: test (-1); repo https://github.com/AtsuMiyai/UPD

## Metrics

- `Dual accuracy` **(primary)** — range: [0, 1]
  - The combined accuracy of correctly answering standard solvable questions and correctly identifying/refusing unsolvable questions. Calculated as the proportion of instances where the model either provides the correct answer (for solvable) or correctly selects a refusal option (for unsolvable).

## Input / output format

**Input**: An image paired with a multiple-choice question. For unsolvable instances, the question or options are modified to create absent answers, incompatible answer sets, or image-question mismatches.

**Output**: A single selected option from the provided choices, or an explicit refusal (e.g., 'None of the above').

## Scoring recipe

```python
def compute_dual_accuracy(predictions, golds, is_unsolvable):
    correct = 0
    for pred, gold, unsolvable in zip(predictions, golds, is_unsolvable):
        if unsolvable:
            if pred == gold:  # gold is the refusal option
                correct += 1
        else:
            if pred == gold:
                correct += 1
    return correct / len(predictions)
```

## Common pitfalls

- High accuracy on standard benchmarks like MMBench does not predict performance on unsolvable detection; correlation is very low.
- Refusal capability is highly sensitive to model size and training data; open-source models often lack refusal alignment compared to closed-source ones.
- Prompting techniques like Chain-of-Thought or self-reflection can improve refusal for some models but may degrade performance for others depending on their specific bottlenecks.

## Evidence (verbatim from paper)

> Table 1 presents the overall Dual accuracies. Also, we show the Standard and UPD accuracies for some LMMs in Fig. 3. In Fig. 4, we show the radar charts of InternVL2-40B and GPT-4o for ability-wise fine-grained analysis.

## Citation

```bibtex
@misc{miyai2024unsolvable,
  title={Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models},
  author={Miyai et al. (2024)},
  year={2024},
  note={arXiv:2403.20331}
}
```

- arXiv: 2403.20331