# Morehopqa Eval

> Evaluates multi-step reasoning capabilities beyond simple information extraction in question answering. It probes models' ability to perform arithmetic, commonsense, and symbolic reasoning by extending standard multi-hop questions with additional reasoning layers. Use when the user wants to benchmark on MoreHopQA, or asks about evaluating this task. Reports EM.

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

---


# morehopqa-eval

> MoreHopQA: More Than Multi-hop Reasoning — Schnitzler et al. (2024) (arXiv:2406.13397, 2024)

## What this evaluates

Evaluates multi-step reasoning capabilities beyond simple information extraction in question answering. It probes models' ability to perform arithmetic, commonsense, and symbolic reasoning by extending standard multi-hop questions with additional reasoning layers.

## Datasets

- **MoreHopQA** — total 1118; splits: test (1118); repo https://github.com/Alab-NII/morehopqa

## Metrics

- `EM` **(primary)** — range: [0, 1]
  - Exact Match score: 1 if the postprocessed model-generated answer exactly matches the preprocessed ground-truth answer, 0 otherwise. Averaged over the dataset.
- `F1` — range: [0, 1]
  - Token-level F1 score computed between the postprocessed model-generated answer and the preprocessed ground-truth answer.

## Input / output format

**Input**: Instruction prompt containing the question, supporting context, and optional few-shot examples. Models are instructed to output the final answer enclosed in <answer> tags.

**Output**: Text string enclosed between <answer> and </answer> tags.

## Scoring recipe

```python
def compute_metrics(predictions, ground_truths):
    em_scores, f1_scores = [], []
    for pred, gt in zip(predictions, ground_truths):
        pred_str = extract_between(pred, '<answer>', '</answer>')
        pred_proc = postprocess_to_string(pred_str)
        gt_proc = preprocess_string(gt)
        em_scores.append(1.0 if pred_proc == gt_proc else 0.0)
        f1_scores.append(token_f1(pred_proc, gt_proc))
    return sum(em_scores) / len(em_scores), sum(f1_scores) / len(f1_scores)
```

## Common pitfalls

- Models often exhibit shortcut reasoning (answering the initial question correctly but failing sub-questions), which requires separate tracking rather than relying solely on overall accuracy.
- The evaluation mandates specific postprocessing (NER/datatype conversion) before comparison; skipping this step yields artificially low EM/F1 scores.
- Gemma-7B frequently outputs refusal strings like 'I cannot answer', which must be filtered or handled to prevent skewing the average performance.

## Evidence (verbatim from paper)

> We then attempt to convert this string into the respective built-in python datatype for the answer type, either directly or with the help of Named Entity Recognition, and convert it back to a default string representation. We then report the EM and F1 scores on the tokens between the preprocessed ground-truth answer and the postprocessed model-generated answer.

## Citation

```bibtex
@misc{schnitzler2024morehopqa,
  title={MoreHopQA: More Than Multi-hop Reasoning},
  author={Schnitzler et al. (2024)},
  year={2024},
  note={arXiv:2406.13397}
}
```

- arXiv: 2406.13397