Datasets:

R0k1e
/

SurveyEval

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Recent Advances in Direct Speech-to-text Translation	Chen Xu, Rong Ye, Qianqian Dong, Chengqi Zhao, Tom Ko, Mingxuan Wang, Tong Xiao, Jingbo Zhu	ZhuRAIDST	http://arxiv.org/abs/2306.11646v1	"Recently, speech-to-text translation has attracted more and more attention\nand many studies have e(...TRUNCATED)	955	http://arxiv.org/src/2306.11646v1	output/download_papers/2306.11646v1/2306.11646v1	http://arxiv.org/pdf/2306.11646v1	output/download_papers/2306.11646v1/2306.11646v1.pdf	11,550	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	33,641	"# Recent Advances in Direct Speech-to-text Translation \n\nChen $\\mathbf{Xu}^{1*}$ , Rong $\\math(...TRUNCATED)	[]	[{"title":"Speech translation: coupling of recognition and translation","authors":null,"bibkey":"Ney(...TRUNCATED)	1	"# References \n\n[Anastasopoulos and Chiang, 2018] Antonios Anastasopoulos and David Chiang. Tied (...TRUNCATED)	["## Acoustic and Linguistic Features","### Signal Processing","### Pre-trained Representation","###(...TRUNCATED)	1	1
"A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectiv(...TRUNCATED)	Nils Rethmeier, Isabelle Augenstein	AugensteinAPOCPILPML	http://arxiv.org/abs/2102.12982v1	"Modern natural language processing (NLP) methods employ self-supervised\npretraining objectives suc(...TRUNCATED)	1,484	http://arxiv.org/src/2102.12982v1	output/download_papers/2102.12982v1/2102.12982v1	http://arxiv.org/pdf/2102.12982v1	output/download_papers/2102.12982v1/2102.12982v1.pdf	39,113	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	36,001	"# A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspect(...TRUNCATED)	[]	[{"title":"A tutorial on energy-based learning","authors":null,"bibkey":"LECUN_EBM_AND_CONTRASTIVE",(...TRUNCATED)	1	"# References \n\n[Aroca-Ouellette and Rudzicz, 2020] Ste´phane ArocaOuellette and Frank Rudzicz. (...TRUNCATED)	["## Introduction","## Contrastive Learning Concepts and Benefits","### Noise Contrastive Estimation(...TRUNCATED)	1	1
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions	Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li	LiETDASOTMAF	http://arxiv.org/abs/2311.09008v1	"End-to-end task-oriented dialogue (EToD) can directly generate responses in\nan end-to-end fashion (...TRUNCATED)	1,367	http://arxiv.org/src/2311.09008v1	output/download_papers/2311.09008v1/2311.09008v1	http://arxiv.org/pdf/2311.09008v1	output/download_papers/2311.09008v1/2311.09008v1.pdf	28,140	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	42,533	"# End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions \n\nLibo $\(...TRUNCATED)	[]	[{"title":"Gpt-4 technical report","authors":null,"bibkey":"OpenAI2023GPT4TR","bibitem":"@article{Op(...TRUNCATED)	1	"# References \n\nYuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, John Kernion, Andy Jo(...TRUNCATED)	["## Introduction","## Background","### Modularly EToD","### Fully End-to-end Task-oriented Dialogue(...TRUNCATED)	1	1
Modern Question Answering Datasets and Benchmarks: A Survey	Zhen Wang	WangMQADABAS	http://arxiv.org/abs/2206.15030v1	"Question Answering (QA) is one of the most important natural language\nprocessing (NLP) tasks. It a(...TRUNCATED)	724	http://arxiv.org/src/2206.15030v1	output/download_papers/2206.15030v1/2206.15030v1	http://arxiv.org/pdf/2206.15030v1	output/download_papers/2206.15030v1/2206.15030v1.pdf	36,721	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	39,708	"# Modern Question Answering Datasets and Benchmarks: A Survey \n\nZhen Wang Delft University of Te(...TRUNCATED)	[]	[{"title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","authors"(...TRUNCATED)	1	"# References \n\nManoj Acharya, Kushal Kafle, and Christopher Kanan. 2019. Tallyqa: Answering comp(...TRUNCATED)	["## Introduction","## Textual Question Answering","### Reading Comprehension","### Reasoning","### (...TRUNCATED)	1	1
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension	Xanh Ho, Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa	AizawaASOMAMRSIM	http://arxiv.org/abs/2209.01824v2	"The issue of shortcut learning is widely known in NLP and has been an\nimportant research focus in (...TRUNCATED)	872	http://arxiv.org/src/2209.01824v2	output/download_papers/2209.01824v2/2209.01824v2	http://arxiv.org/pdf/2209.01824v2	output/download_papers/2209.01824v2/2209.01824v2.pdf	53,600	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	47,043	"# A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension \n\nXa(...TRUNCATED)	[]	[{"title":"Numerical reasoning in machine reading comprehension tasks: are we there yet?","authors"(...TRUNCATED)	1	"# References \n\nHadeel Al-Negheimish, Pranava Madhyastha, and Alessandra Russo. 2021. Numerical r(...TRUNCATED)	["## Introduction","## Background","### Machine Reading Comprehension Task","### Definitions and Ter(...TRUNCATED)	1	1
A Survey of Confidence Estimation and Calibration in Large Language Models	Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, Iryna Gurevych	GurevychASOCEACILL	http://arxiv.org/abs/2311.08298v2	"Large language models (LLMs) have demonstrated remarkable capabilities across\na wide range of task(...TRUNCATED)	795	http://arxiv.org/src/2311.08298v2	output/download_papers/2311.08298v2/2311.08298v2	http://arxiv.org/pdf/2311.08298v2	output/download_papers/2311.08298v2/2311.08298v2.pdf	64,144	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	47,489	"# A Survey of Confidence Estimation and Calibration in Large Language Models \n\nJiahui Geng1, Fen(...TRUNCATED)	[]	[{"title":"Vicuna: An open-source chatbot impressing gpt-4 with 90\\%* chatgpt quality","authors":"C(...TRUNCATED)	0.992481	"# References \n\nAlfonso Amayuelas, Liangming Pan, Wenhu Chen, and William Wang. 2023. Knowledge o(...TRUNCATED)	["## Introduction","## Preliminaries and Background","### Basic Concepts","### Metrics and Methods",(...TRUNCATED)	0.992481	0.992481
A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models	Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song	SongASOCTGUTPL	http://arxiv.org/abs/2201.05337v5	"Controllable Text Generation (CTG) is emerging area in the field of natural\nlanguage generation (N(...TRUNCATED)	1,521	http://arxiv.org/src/2201.05337v5	output/download_papers/2201.05337v5/2201.05337v5	http://arxiv.org/pdf/2201.05337v5	output/download_papers/2201.05337v5/2201.05337v5.pdf	104,082	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	93,476	"# A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models \n\(...TRUNCATED)	[]	[{"title":"Being Polite: Modeling Politeness Variation in a Personalized Dialog Agent","authors":"Fi(...TRUNCATED)	0.988372	"# REFERENCES \n\n[1] Ali Amin-Nejad, Julia Ive, and Sumithra Velupillai. 2020. Exploring Transform(...TRUNCATED)	["## Introduction","## An Introduction to Controllable Text Generation and Pre-trained Language Mode(...TRUNCATED)	0.988372	0.988372
Measure and Improve Robustness in NLP Models: A Survey	Xuezhi Wang, Haohan Wang, Diyi Yang	YangMAIRINMAS	http://arxiv.org/abs/2112.08313v2	"As NLP models achieved state-of-the-art performances over benchmarks and\ngained wide applications,(...TRUNCATED)	1,074	http://arxiv.org/src/2112.08313v2	output/download_papers/2112.08313v2/2112.08313v2	http://arxiv.org/pdf/2112.08313v2	output/download_papers/2112.08313v2/2112.08313v2.pdf	43,654	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	40,386	"# Measure and Improve Robustness in NLP Models: A Survey \n\nXuezhi Wang Google Research xuezhiw@g(...TRUNCATED)	[]	[{"title":"Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment","au(...TRUNCATED)	0.988889	"# References \n\nAlberto Abad, Peter Bell, Andrea Carmantini, and Steve Renals. 2020. Cross lingua(...TRUNCATED)	["## Introduction","## Definitions of Robustness in NLP","### Robustness against Adversarial Attacks(...TRUNCATED)	0.994444	0.983333
Neural Entity Linking: A Survey of Models Based on Deep Learning	Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, Chris Biemann	AlexNELASOMBOD	http://arxiv.org/abs/2006.00575v4	"This survey presents a comprehensive description of recent neural entity\nlinking (EL) systems deve(...TRUNCATED)	1,232	http://arxiv.org/src/2006.00575v4	output/download_papers/2006.00575v4/2006.00575v4	http://arxiv.org/pdf/2006.00575v4	null	160,174	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	138,547	"# Neural Entity Linking: A Survey of Models Based on Deep Learning \n\nÖzge Sevgili a,\\*, Artem (...TRUNCATED)	[]	[{"title":"A Neural Probabilistic Language Model","authors":null,"bibkey":"bengio","bibitem":"@artic(...TRUNCATED)	0.985782	"# References \n\n[1] F. Abel, C. Hauff, G.-J. Houben, R. Stronkman and K. Tao, Twitcident: Fightin(...TRUNCATED)	["## Introduction","### Goal and Scope of this Survey","### Article Collection Methodology","### Pre(...TRUNCATED)	0.985782	0.981043
A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond	Yisheng Xiao, Lijun Wu, Junliang Guo, Juntao Li, Min Zhang, Tao Qin, Tie-yan Liu	LiuASONGFNMTA	http://arxiv.org/abs/2204.09269v2	"Non-autoregressive (NAR) generation, which is first proposed in neural\nmachine translation (NMT) t(...TRUNCATED)	1,733	http://arxiv.org/src/2204.09269v2	output/download_papers/2204.09269v2/2204.09269v2	http://arxiv.org/pdf/2204.09269v2	output/download_papers/2204.09269v2/2204.09269v2.pdf	145,774	"\\documentclass{article}\\usepackage[T1]{fontenc}\\usepackage[utf8]{inputenc}\\usepackage{lmodern}\(...TRUNCATED)	136,488	"# A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond \n\nYisheng (...TRUNCATED)	[]	[{"title":"An introduction to machine translation","authors":null,"bibkey":"somers1992introduction",(...TRUNCATED)	0.979339	"# REFERENCES \n\n[1] W. J. Hutchins and H. L. Somers, An introduction to machine translation. Lond(...TRUNCATED)	["## Introduction","## Overview of AT and NAT Models","### Comparison","### The Main Challenge of NA(...TRUNCATED)	0.979339	0.979339

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SurveyEval: An Evaluation Benchmark Dataset for LLM $\times$ MapReduce-V2

SurveyEval stands as a pioneering evaluation benchmark dataset specifically designed for LLM$\times$MapReduce-V2 in the realm of computer science. If you intend to utilize it for evaluation purposes or incorporate it into the creation of a survey, kindly refer to our Github for detailed instructions and guidelines and refer to us paper.

Dataset Uniqueness

To the best of our knowledge, SurveyEval is the first dataset of its kind that meticulously pairs surveys with comprehensive reference papers. We have curated a collection of 384 survey papers from various online sources. Collectively, these papers cite over 26,000 references, providing a rich and extensive knowledge repository for research and evaluation endeavors.

Comparative Analysis with Other Datasets

The following table offers a multi-dimensional comparison between our SurveyEval dataset and other relevant datasets in the field. Through a careful examination of the table, it becomes evident that SurveyEval distinguishes itself as the sole dataset that not only offers complete reference information but also boasts high full content coverage. This unique combination makes it an invaluable resource for researchers and practitioners seeking a comprehensive and reliable dataset for their work.

Composition of the Test Split

The table below provides a detailed breakdown of all 20 surveys included in the test split of the SurveyEval dataset. This overview offers insights into the diversity and scope of the surveys, enabling users to better understand the dataset's composition and tailor their research accordingly.

Citation and Usage Guidelines

Please note that the SurveyEval dataset is intended exclusively for research and educational purposes. It should not be misconstrued as representing the opinions or views of the dataset's creators, owners, or contributors. When using the dataset in your work, we kindly request that you cite it appropriately using the following BibTeX entry:

@misc{wang2025llmtimesmapreducev2entropydrivenconvolutionaltesttime,
      title={LLM$\times$MapReduce-V2: Entropy-Driven Convolutional Test-Time Scaling for Generating Long-Form Articles from Extremely Long Resources}, 
      author={Haoyu Wang and Yujia Fu and Zhu Zhang and Shuo Wang and Zirui Ren and Xiaorong Wang and Zhili Li and Chaoqun He and Bo An and Zhiyuan Liu and Maosong Sun},
      year={2025},
      eprint={2504.05732},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.05732}, 
}

We hope that SurveyEval proves to be a valuable asset in your research endeavors, and we welcome your feedback and contributions to further enhance the dataset's utility and impact.

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