lime-nlp/Synthetic_Unanswerable_Math
Dataset Card for Synthetic Unanswerable Math (SUM) Dataset Summary Synthetic Unanswerable Math (SUM) is a dataset of high-quality, implicitly unanswerable math problems constructed to probe and improve the refusal behavior of large language models (LLMs). The goal is to teach models to identify when a problem cannot be answered due to incomplete, ambiguous, or contradictory information, and respond with epistemic humility (e.g., \boxed{I don't know}). Each entry… See the full description on the dataset page: https://huggingface.co/datasets/lime-nlp/Synthetic_Unanswerable_Math.
| Author / organization | lime-nlp |
|---|---|
| License (as declared) | mit |
| Downloads | 265 (as reported by Hugging Face) |
| Likes | 19 |
| Created | 13 May 2025 |
| Last updated at source | 21 May 2025 |
Popularity, last 2 days
265 now (-8 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 17 Oct 2026 Description: Dataset Card for Synthetic Unanswerable Math (SUM) Dataset Summary Synthetic Unanswerable Math (SUM) is a dataset of high-quality, implicitly unanswerable math problems constructed to probe and improve the refusal behavior of large language → Dataset Card for Synthetic Unanswerable Math (SUM) Dataset Summary Synthetic Unanswerable Math (SUM) is a dataset of high-quality, implicitly unanswerable math problems constructed to probe and improve the refusal behavior of large language
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Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.