---
license: [cc-by-4.0, apache-2.0]
language: [en]
task_categories: [text-classification, question-answering]
tags: [rag, evidence-sufficiency, multi-hop-qa, counterfactual, benchmark, chaincheck]
pretty_name: ChainCheck
---

# ChainCheck

Counterfactual controls for evaluating evidence-sufficiency scorers in retrieval-augmented QA. From *ChainCheck: When Near-Perfect Evidence-Sufficiency Scores Fail to Distinguish Chain-Sensitive Systems* (preprint forthcoming).

A sufficiency scorer can look near-perfect on an ordinary benchmark by reacting to any edit in the passages. ChainCheck adds a matched control so the two can be told apart:

| Cell | Chain | Edit |
|---|---|---|
| A | intact | none |
| B | intact | consistent bridge-entity swap |
| D | broken (later-hop substitution) | yes |

Nominal = AUC(A > D), chain effect = AUC(B > D) − 0.5, edit effect = AUC(A > B) − 0.5, chain selectivity Σ = CE − |EE|. These are matched contrasts, not an additive decomposition.

## Files

| File | Rows | Source license |
|---|---|---|
| `musique_test.jsonl.gz` | 1716 | MuSiQue (Trivedi et al., TACL 2022), CC BY 4.0 |
| `musique_confirmation.jsonl.gz` | 678 | MuSiQue (Trivedi et al., TACL 2022), CC BY 4.0 |
| `twowiki_replication.jsonl.gz` | 1392 | 2WikiMultiHopQA (Ho et al., COLING 2020), Apache-2.0 |

Each row is one (question, passage set) item: `item_id`, `pair_id`, `variant` (`real`, or `fict` for a synthetic replacement entity), `cell` (A/B/D), `query`, `passages`, `answer`, `bridge`, `replacement`, `chain`, `edit`, `earlier_support`, `later_support`, `label_sufficient`, `cb_27b_correct` (Qwen3.8-27B answered closed-book; the paper excludes these pairs) and `source`. Only quadruples that pass the Q1 to Q9 validator are included. Labels follow mechanically from source provenance; no human or LLM labels.

## Evaluate your scorer

```bash
python chaincheck_eval.py --data twowiki_replication.jsonl.gz --scores my_scores.jsonl --exclude-cb27b
```

`my_scores.jsonl` holds one `{"item_id": ..., "score": float}` per item (higher = more sufficient). The script needs only numpy and reproduces the paper's reference-judge numbers exactly.

## Licenses

Derived from MuSiQue (CC BY 4.0) and 2WikiMultiHopQA (Apache-2.0); passage text originates from Wikipedia (CC BY-SA). Each file keeps its source license; cite the original datasets alongside this one.

## Models

Judges trained with these controls: [ChainCheck-Judge collection](https://huggingface.co/ThakiCloud).