---
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- rag
- cacheblend
- multi-hop
- kv-cache
size_categories:
- n<1K
---

# CacheBlend RAG Extended

Multi-hop QA splits derived from MuSiQue and 2WikiMQA (as bundled in the
[official CacheBlend repo](https://github.com/YaoJiayi/CacheBlend)), augmented
with extra distractor chunks per query so that each example carries **20
context passages** instead of the original 10.

Designed to amplify the contrast between `full_reuse` (no cross-chunk
attention -> quality drop) and `cacheblend` (selective KV recompute ->
quality recovers) while preserving the multi-hop questions and gold
short answers.

## Splits

| split | n | chunks/query | ~tokens/chunk | gold preserved vs source | structural preservation | CacheBlend ready |
|---|---|---|---|---|---|---|
| `musique` | 150 | 20 | 598 | 100.00% | 100% | PASS |
| `wikimqa` | 200 | 20 | 560 | 103.81% | 100% | PASS |

## Schema

Each row matches the bundled CacheBlend JSON format:

```json
{
  "question": "Where was the author of Hannibal and Scipio educated at?",
  "ctxs": [
    {"title": "<wiki title>", "text": "<512-token passage>"},
    ...  // 20 entries
  ],
  "answers": ["Exeter College"]
}
```

## How it was built

1. Start from `musique_s.json` (150 ex) and `wikimqa_s.json` (200 ex) — both
   already have the multi-hop questions and 10 gold/distractor passages
   per query that the CacheBlend paper used.
2. For each query, sample 10 additional chunks from OTHER queries in the
   same source (no chunk is reused twice within a query; titles deduped).
3. Shuffle the resulting 20 chunks so gold evidence is not always first.
4. Verify (per row): (i) chunks/query == 20, (ii) at least one gold-answer
   substring is preserved across the 20 chunks.

Reproduce with `scripts/build_cacheblend_dataset.py --extra 10` in
[mkim0628/experiment-reproducer](https://github.com/mkim0628/experiment-reproducer).

## Suitability gates

The build script runs three CacheBlend-relevance checks. All splits must
pass before upload:

- `chunks_per_query_ge_10`: at least 10 passages so KV reuse pays off
- `gold_preservation_ratio_ge_0.95`: ≥95 % of queries still have the
  gold-answer substring after distractor injection
- `chunk_size_within_paper_range_300_800_tokens`: chunk length matches
  the paper's 512-token spec

See `suitability_report.json` for the per-split numbers.