dataset stringlengths 1 82 | model_name stringlengths 0 150 | paper_title stringlengths 22 175 | paper_date timestamp[ns]date 2023-05-02 00:00:00 2024-12-12 00:00:00 | paper_url stringlengths 32 35 | code_links listlengths 1 1 | prompts stringlengths 115 330 | answer stringlengths 1 22 | paper_text stringlengths 83 737k | year_bin stringclasses 2
values | benchmark_split stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|
ActivityNet-QA | TESTA (ViT-B/16) | TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding | 2023-10-29T00:00:00 | https://arxiv.org/abs/2310.19060v1 | [
"https://github.com/renshuhuai-andy/testa"
] | In the paper 'TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding', what Accuracy score did the TESTA (ViT-B/16) model get on the ActivityNet-QA dataset
| 45 | Title: TESTA: Temporal-Spatial Token Aggregationfor Long-form Video-Language Understanding
Abstract: AbstractLarge-scale video-language pre-training has made remarkable strides in advancing video-language understanding tasks. However, the heavy computational burden of video encoding remains a formidable efficiency bot... | 2023 | public |
Youtube-VIS 2022 Validation | CTVIS (ResNet-50) | CTVIS: Consistent Training for Online Video Instance Segmentation | 2023-07-24T00:00:00 | https://arxiv.org/abs/2307.12616v1 | [
"https://github.com/kainingying/ctvis"
] | In the paper 'CTVIS: Consistent Training for Online Video Instance Segmentation', what mAP_L score did the CTVIS (ResNet-50) model get on the Youtube-VIS 2022 Validation dataset
| 39.4 | Title: CTVIS: Consistent Training for Online Video Instance Segmentation
Abstract: AbstractThe discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is directly supervised by the contrastive loss computed ... | 2023 | public |
CIFAR-100 (partial ratio 0.05) | ILL | "Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurati(...TRUNCATED) | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.12715v4 | [
"https://github.com/hhhhhhao/general-framework-weak-supervision"
] | "In the paper 'Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Lab(...TRUNCATED) | 74.58 | "Title: Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Conf(...TRUNCATED) | 2023 | public |
VoxCeleb1 | ReDimNet-B4-LM (6.3M) | Reshape Dimensions Network for Speaker Recognition | 2024-07-25T00:00:00 | https://arxiv.org/abs/2407.18223v2 | [
"https://github.com/IDRnD/ReDimNet"
] | "In the paper 'Reshape Dimensions Network for Speaker Recognition', what EER score did the ReDimNet-(...TRUNCATED) | 0.51 | "Title: Reshape Dimensions Network for Speaker Recognition\n\nAbstract: AbstractIn this paper, we pr(...TRUNCATED) | 2024-2025 | public |
WebApp1K-React | llama-v3p1-405b-instruct | Insights from Benchmarking Frontier Language Models on Web App Code Generation | 2024-09-08T00:00:00 | https://arxiv.org/abs/2409.05177v1 | [
"https://github.com/onekq/webapp1k"
] | "In the paper 'Insights from Benchmarking Frontier Language Models on Web App Code Generation', what(...TRUNCATED) | 0.302 | "Title: Insights from Benchmarking Frontier Language Models on Web App Code Generation\n\nAbstract: (...TRUNCATED) | 2024-2025 | public |
ImageNet | GTP-DeiT-B/P8 | GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation | 2023-11-06T00:00:00 | https://arxiv.org/abs/2311.03035v2 | [
"https://github.com/ackesnal/gtp-vit"
] | "In the paper 'GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation', what Top 1(...TRUNCATED) | 81.5% | "Title: GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation\n\nAbstract: Abstra(...TRUNCATED) | 2023 | public |
COCO-Stuff Labels-to-Photos | SCDM | Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis | 2024-02-26T00:00:00 | https://arxiv.org/abs/2402.16506v3 | [
"https://github.com/mlvlab/scdm"
] | "In the paper 'Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis', what mI(...TRUNCATED) | 38.1 | "Title: Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis\n\nAbstract: Abs(...TRUNCATED) | 2024-2025 | public |
GA1457 | DiffAug | "DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data (...TRUNCATED) | 2023-09-10T00:00:00 | https://arxiv.org/abs/2309.07909v2 | [
"https://github.com/zangzelin/code_diffaug"
] | "In the paper 'DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusi(...TRUNCATED) | 92.7 | "Title: Boosting Unsupervised Contrastive Learning Using Diffusion-Based Data Augmentation From Scra(...TRUNCATED) | 2023 | public |
GoPro | M3SNet | A Mountain-Shaped Single-Stage Network for Accurate Image Restoration | 2023-05-09T00:00:00 | https://arxiv.org/abs/2305.05146v1 | [
"https://github.com/Tombs98/M3SNet"
] | "In the paper 'A Mountain-Shaped Single-Stage Network for Accurate Image Restoration', what PSNR sco(...TRUNCATED) | 33.74 | "Title: A Mountain-Shaped Single-Stage Network for Accurate Image Restoration\n\nAbstract: AbstractI(...TRUNCATED) | 2023 | public |
ChEBI-20 | MolReGPT (GPT-4-0413) | "Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGP(...TRUNCATED) | 2023-06-11T00:00:00 | https://arxiv.org/abs/2306.06615v2 | [
"https://github.com/phenixace/molregpt"
] | "In the paper 'Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Mo(...TRUNCATED) | 59.3 | "Title: Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A(...TRUNCATED) | 2023 | public |
End of preview. Expand in Data Studio
ARIA Search Benchmark v2
The ARIA Search Benchmark is part of the ARIA benchmark suite, a collection of closed-book benchmarks probing the ML knowledge that frontier models have internalized during training. This dataset tests whether models can answer factual questions about ML research papers, models, datasets, and benchmark results without access to external retrieval.
Dataset Summary
- Size: 3,517 question-answer pairs
- Split:
benchmark - Paper date range: May 2023 to December 2024
- Coverage: Spans models, datasets, and metrics across CV, NLP, audio, video, and multimodal domains
Dataset Structure
| Field | Type | Description |
|---|---|---|
dataset |
string | Benchmark dataset referenced |
model_name |
string | Model being evaluated |
paper_title |
string | Source paper title |
paper_date |
timestamp | Publication date |
paper_url |
string | ArXiv paper URL |
code_links |
list[string] | GitHub repository links |
prompts |
string | Question/prompt text |
answer |
string | Ground-truth answer |
paper_text |
string | Full paper text |
year_bin |
string | Year category for stratified evaluation |
benchmark_split |
string | Benchmark split identifier |
Usage
from datasets import load_dataset
ds = load_dataset("AlgorithmicResearchGroup/aria-search-benchmark_v2-public", split="benchmark")
for example in ds.select(range(5)):
print(f"Q: {example['prompts']}")
print(f"A: {example['answer']}")
print(f"Paper: {example['paper_title']}")
print()
Related Resources
Citation
@misc{aria_search_benchmark_v2,
title={ARIA Search Benchmark v2},
author={Algorithmic Research Group},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/datasets/AlgorithmicResearchGroup/aria-search-benchmark_v2-public}
}
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