metadata
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
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
@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}
}