datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
in-context-grid-reasoning
In-Context Grid Reasoning (ICGR)
A small, fully synthetic benchmark for demonstration-conditioned rule induction:
each task shows 2–4 (input grid → output grid) support pairs that share one
hidden transformation, and the model must apply the same transformation to a
held-out query input.
It targets the same behaviour probed by recent in-context / latent-reasoning work
on ARC-AGI (e.g. BDH-CQ: In-Context Learning with Recurrent Latent Reasoning,
arXiv:2608.09888), but is… See the full description on the dataset page: https://huggingface.co/datasets/WhySoCodius/in-context-grid-reasoning.in-car-context-benchmark
Benchmarking contextual understanding for in-car conversational systems
This dataset contains the complete evaluation benchmarks, user utterances, venue recommendations, and failure-annotated responses for evaluating in-car Conversational Question Answering (ConvQA) systems.
Official Code & Implementation: github.com/saydemr/judgebench
Paper (Journal of Systems and Software, 2026): doi.org/10.1016/j.jss.2026.112915 or arxiv.org/abs/2512.12042
📌 Quickstart
from… See the full description on the dataset page: https://huggingface.co/datasets/saydemr/in-car-context-benchmark.Maintainng-Context-in_Dialogue
🇰🇿 Kazakh Multi-turn Cognitive Dialogue Dataset
📖 Overview
This dataset consists of 200 high-depth, multi-turn conversational samples in the Kazakh language.
📊 Dataset Statistics
General Metrics
Metric
Count
Total Samples
200
Total Words (approx.)
52,780
Avg. Words per Sample
263
Word Count Distribution (Per Field)
The following table details the distribution of word counts across different… See the full description on the dataset page: https://huggingface.co/datasets/farabi-lab/Maintainng-Context-in_Dialogue.