qg-eval-multi-fresh

Held-out evaluation passages for olaverse/mist-qg-1.5b — provably never seen during training, used to compute the model's published round-trip keep-rate.

Dataset Summary

625 passages (~25 per language, 25 languages), sampled from the same source as the training data but explicitly deduplicated against every passage used to train mist-qg-1.5b, so scores on this set measure generalization rather than memorization.

Data Fields

Field	Type	Description
passage	string	Source text, unseen during training
language	string	ISO 639-3 code

No questions field — this dataset intentionally ships passages only. Evaluation is done by generating questions with the model under test and checking round-trip retrieval, not by comparing against a fixed reference answer.

Data Instance

{
  "passage": "Photosynthesis converts sunlight into chemical energy in plants, using carbon dioxide and water to produce glucose and oxygen.",
  "language": "eng"
}

Dataset Creation

Passages were streamed from the same source as qg-passages-multi (CohereLabs/aya_collection_language_split), using a different shuffle seed. Every candidate was hashed and checked against the hash set of all passages used across every training run of mist-qg-1.5b (both the original and a cleaned variant that was ultimately not published); any match was discarded before sampling.

Intended Use

Evaluating question-generation models via round-trip self-consistency: generate questions on these passages, embed the questions and the passage pool, and check whether each question retrieves its own source passage. This is how mist-qg-1.5b's published metric was computed — see its model card for the full per-language table and the embedder used to score it.

This is an in-house benchmark, not a third-party or standardized one — treat scores against it as directly comparable only across models evaluated the same way (same embedder, same distractor pool size, same top-k).

Licensing

Apache-2.0.

Citation

@misc{qg-eval-multi-fresh,
  title  = {qg-eval-multi-fresh},
  author = {Olaverse},
  year   = {2026},
  url    = {https://huggingface.co/datasets/olaverse/qg-eval-multi-fresh}
}