| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - math |
| - calibration |
| - rl |
| pretty_name: CONCORD training prompts |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # CONCORD training prompts |
|
|
| 1600 math prompts used to train the CONCORD models from |
|
|
| > *CONCORD: Label-Free Calibration of Verbalized LLM Confidence via Rollout Consistency.* |
| > Maximilian Schall, Gerard de Melo, Padhraic Smyth. **COLM 2026.** |
|
|
| Sampled uniformly across difficulty levels from |
| [`OpenDataArena/ODA-Math-460k`](https://huggingface.co/datasets/OpenDataArena/ODA-Math-460k) |
| with seed 42. Only the prompt column (`question`) is retained — CONCORD is |
| label-free, so reference answers are intentionally not included. |
|
|
| - Source: `OpenDataArena/ODA-Math-460k` |
| - Subset size: 1600 |
| - Columns: `question` |
| - Sampling: uniform over `difficulty`, seed 42 |
|
|
| If you need reference answers, difficulty labels, or the full dataset, |
| use the original source directly. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{schall2026concord, |
| title = {{CONCORD}: Label-Free Calibration of Verbalized {LLM} Confidence via Rollout Consistency}, |
| author = {Schall, Maximilian and de Melo, Gerard and Smyth, Padhraic}, |
| booktitle = {Conference on Language Modeling (COLM)}, |
| year = {2026} |
| } |
| ``` |
|
|
