Narrativeqa Eval

---
name: narrativeqa-eval
description: Evaluates English long-document retrieval on complex, narrative-style questions, probing deep comprehension and information extraction from lengthy texts. Use when the user wants to benchmark on NarrativeQA, or asks about evaluating this task. Reports nDCG@10.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2402.03216
  bibtex_key: [REDACTED]
  confidence: high
---

# narrativeqa-eval

> M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation — Chen et al. (2024) (arXiv:2402.03216, 2024)

## What this evaluates

Evaluates English long-document retrieval on complex, narrative-style questions, probing deep comprehension and information extraction from lengthy texts.

## Datasets

- **NarrativeQA** — total ?; splits: test (-1)

## Metrics

- `nDCG@10` **(primary)** — range: [0, 1]
  - Normalized Discounted Cumulative Gain at rank 10, assessing the ranking quality of retrieved narrative documents.

## Input / output format

**Input**: Complex questions and corresponding English narrative documents.

**Output**: A ranked list of retrieved documents.

## Scoring recipe

```python
def compute_ndcg_at_10(retrieved_ids, relevant_ids):
    dcg = 0.0
    for i, doc_id in enumerate(retrieved_ids[:10]):
        if doc_id in relevant_ids:
            dcg += 1.0 / math.log2(i + 2)
    idcg = sum(1.0 / math.log2(i + 2) for i in range(min(len(relevant_ids), 10)))
    return dcg / idcg if idcg > 0 else 0.0
```

## Common pitfalls

- Performance advantage over baselines grows with sequence length, indicating sensitivity to input context window.
- Only evaluates English documents, unlike the other benchmarks in the paper.

## Evidence (verbatim from paper)

> We make further analysis with NarrativeQA (Table[4]), where we can make a similar observation as MLDR. Besides, with the growth of sequence length, our method gradually expands its advantage over baseline methods (Figure [5]), which reflects its proficiency in handling long inputs. Table 4: Evaluation on NarrativeQA (nDCG@10).

## Citation

```bibtex
@misc{chen2024m3embedding,
  title={M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
  author={Chen et al. (2024)},
  year={2024},
  note={arXiv:2402.03216}
}
```

- arXiv: 2402.03216