# Indicgenbench Eval

> Evaluates the multilingual and cross-lingual generation capabilities of LLMs across 29 Indic languages, covering summarization, machine translation, and question answering. It probes how model performance scales with language resourcedness, in-context learning, and fine-tuning. Use when the user wants to benchmark on CrossSum-In, Flores-In, XQuAD-In, XorQA-In, or asks about evaluating this task. Reports Character-F1 (ChrF), SQuAD-style Token-F1.

- Skill: `qhjqhj00/indicgenbench-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/indicgenbench-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/indicgenbench-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/indicgenbench-eval

---


# indicgenbench-eval

> IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages — Singh et al. (2024) (arXiv:2404.16816, 2024)

## What this evaluates

Evaluates the multilingual and cross-lingual generation capabilities of LLMs across 29 Indic languages, covering summarization, machine translation, and question answering. It probes how model performance scales with language resourcedness, in-context learning, and fine-tuning.

## Datasets

- **CrossSum-In** — total ?; splits: test (-1)
- **Flores-In** — total ?; splits: test (-1)
- **XQuAD-In** — total ?; splits: test (-1)
- **XorQA-In** — total ?; splits: test (-1)

## Metrics

- `Character-F1 (ChrF)` **(primary)** — range: [0, 1]
  - Computes n-gram overlap between reference and hypothesis at the character level, combining precision and recall into an F1 score. Used for summarization and translation tasks.
- `SQuAD-style Token-F1` **(primary)** — range: [0, 1]
  - Computes token-level precision and recall between the predicted answer span and the gold answer, then returns their harmonic mean. Used for QA tasks.

## Input / output format

**Input**: Task-specific prompts in a few-shot or zero-shot setting. For summarization/translation: source text in one language, target in another. For QA: question and context passage in the target language or English.

**Output**: Generated text: a summary, a translated sentence, or a short answer span.

## Scoring recipe

```python
def compute_chrF(hypothesis, reference):
    # Character n-gram overlap (typically n=6)
    return chrF_score(hypothesis, reference)

def compute_token_f1(pred_span, gold_span):
    pred_tokens = set(pred_span.split())
    gold_tokens = set(gold_span.split())
    if not pred_tokens or not gold_tokens:
        return 0.0
    precision = len(pred_tokens & gold_tokens) / len(pred_tokens)
    recall = len(pred_tokens & gold_tokens) / len(gold_tokens)
    return 2 * precision * recall / (precision + recall)
```

## Common pitfalls

- Token-level metrics like ROUGE and BLEU are unreliable for low-resource Indic languages; Character-F1 must be used instead.
- Translation performance differs significantly by direction (en→xx vs xx→en), with generation often lagging behind understanding.
- High token fertility in low-resource languages reduces the number of in-context examples that fit in the context window, artificially lowering few-shot performance.

## Evidence (verbatim from paper)

> For the cross-lingual summarization and translation tasks, CrossSum-In and Flores-In, we report Character-F1 (ChrF) metric since token-level metrics like ROUGE and BLEU are not reliable for low-resource languages. To stay consistent with existing literature on QA tasks, we report SQuAD-style Token-F1 on our XQuAD-In and XorQA-In QA tasks.

## Citation

```bibtex
@misc{singh2024indicgenbench,
  title={IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages},
  author={Singh et al. (2024)},
  year={2024},
  note={arXiv:2404.16816}
}
```

- arXiv: 2404.16816