Evaluates multilingual mathematical reasoning capability, specifically probing whether models can comprehend and solve Korean math problems by leveraging English-as-pivot reasoning to bridge cross-lingual comprehension gaps. Use when the user wants to benchmark on HRM8K, or asks about evaluating this task. Reports pass@1.
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---
name: hrm8k-eval
description: Evaluates multilingual mathematical reasoning capability, specifically probing whether models can comprehend and solve Korean math problems by leveraging English-as-pivot reasoning to bridge cross-lingual comprehension gaps. Use when the user wants to benchmark on HRM8K, or asks about evaluating this task. Reports pass@1.
metadata:
skill_kind: dataset_eval
source_arxiv: 2501.02448
bibtex_key: [REDACTED]
confidence: medium
---
# hrm8k-eval
> Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap — Ko et al. (2025) (arXiv:2501.02448, 2025)
## What this evaluates
Evaluates multilingual mathematical reasoning capability, specifically probing whether models can comprehend and solve Korean math problems by leveraging English-as-pivot reasoning to bridge cross-lingual comprehension gaps.
## Datasets
- **HRM8K** — total ?; splits: test (-1)
## Metrics
- `pass@1` **(primary)** — range: percent
- Percentage of problems where the model's single generated response matches the ground-truth answer. Evaluated in a pass@1 setting.
## Input / output format
**Input**: Korean-language mathematical word problems or equations.
**Output**: Step-by-step reasoning and final answer, constrained to be generated in Korean.
## Scoring recipe
```python
def compute_pass1(predictions, gold):
correct = 0
for pred, g in zip(predictions, gold):
if extract_final_answer(pred) == g:
correct += 1
return (correct / len(predictions)) * 100
```
## Common pitfalls
- Using extremely low sampling temperatures causes models to revert to English or Chinese instead of the target language.
- Multilingual performance gaps are frequently misattributed to reasoning limitations rather than input comprehension difficulties.
## Evidence (verbatim from paper)
> While lower temperatures are often used in pass@1 settings, we observed that extremely low temperatures sometimes cause models to revert to their preferred language (often English or Chinese). Hence, to maintain the specified response language, we employ a slightly higher temperature with moderate top_p.
## Citation
```bibtex
@misc{ko2025understand,
title={Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap},
author={Ko et al. (2025)},
year={2025},
note={arXiv:2501.02448}
}
```
- arXiv: 2501.02448