README.md

metadata

license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - survey
size_categories:
  - n<1K
extra_gated_prompt: >-
  You agree to not use the dataset to conduct experiments that cause harm to
  human subjects.
extra_gated_fields:
  Company/Organization: text
  Country: country

🧠 SurveyScope

---

🎉 News

• ✅ 2025.06.16 — We release the paper:
  SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation
  → GitHub: FlagOpen/SciSage

---

📚 Overview

SurveyScope is a high-quality benchmark tailored for evaluating the content quality of scientific surveys generated by the SciSage framework. It provides reliable reference material, diverse topic coverage, and human-curated citation data.

---

🏗️ Dataset Construction

The construction pipeline of SurveyScope is illustrated in Figure 1 and includes the following key stages:

• Domain Identification from Existing Benchmarks
  We began by mining open-source academic benchmarks and identifying covered domains using Qwen3-32B with structured prompting.

• Topic Augmentation with Expert & LLM Input
  To ensure domain completeness, we incorporated suggestions from domain experts and LLMs, filling topic gaps and addressing underrepresented fields.

• Paper Selection per Domain
  For each domain, we manually selected highly cited and recent papers from Google Scholar to ensure high quality and recency.

Figure 1: Overview of the SurveyScope construction pipeline.

---

Dataset Details

Category	Research Topic	Paper Title	citation num (250605)	year	url	token num (qwen2.5)
NLP	Speech-to-text Translation	Recent Advances in Direct Speech-to-text Translation	26	2023	http://arxiv.org/abs/2306.11646	17,611
NLP	Contrastive Pretraining in Language Processing	A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives	103	2023	http://arxiv.org/abs/2102.12982v1	18,920
Dialogue Systems	Task-oriented Dialogue Systems	End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions	22	2023	http://arxiv.org/abs/2311.09008v1	36,991
Benchmarking / Evaluation	Question Answering Datasets and Benchmarks	Modern Question Answering Datasets and Benchmarks: A Survey	34	2022	http://arxiv.org/abs/2206.15030v1	20,066
NLP	Reasoning Shortcuts in MRC	A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension	10	2022	http://arxiv.org/abs/2209.01824v2	31,808
LLMs (General)	Confidence Estimation in LLMs	A Survey of Confidence Estimation and Calibration in Large Language Models	75	2023	http://arxiv.org/abs/2311.08298v2	31,777
LLMs (General)	Controllable Text Generation	A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models	402	2023	http://arxiv.org/abs/2201.05337v5	56,627
NLP	Robustness in NLP Models	Measure and Improve Robustness in NLP Models: A Survey	143	2021	http://arxiv.org/abs/2112.08313v2	39,066
NLP	Neural Entity Linking	Neural Entity Linking: A Survey of Models Based on Deep Learning	204	2022	http://arxiv.org/abs/2006.00575v4	108,546
NLP	Non-Autoregressive Generation in NMT	A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond	110	2023	http://arxiv.org/abs/2204.09269v2	77,863
LLMs Safety	Bias and Fairness in LLMs	Bias and Fairness in Large Language Models: A Survey	705	2024	http://arxiv.org/abs/2309.00770v3	110,790
LLMs Efficiency	NLP Efficiency	Efficient Methods for Natural Language Processing: A Survey	134	2023	http://arxiv.org/abs/2209.00099v2	63,709
LLMs Efficiency	LLM Efficiency	The Efficiency Spectrum of Large Language Models: An Algorithmic Survey	27	2023	http://arxiv.org/abs/2312.00678v2	70,382
Medical / Biomedical	Biomedical Language Models	Pre-trained Language Models in Biomedical Domain: A Systematic Survey	213	2023	http://arxiv.org/abs/2110.05006v4	103,620
NLP	Code-Switching in NLP	The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges	56	2022	http://arxiv.org/abs/2212.09660v2	93,129
Dialogue Systems	Proactive Dialogue Systems	A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects	56	2023	http://arxiv.org/abs/2305.02750v2	19,064
Dialogue Systems	Reinforcement Learning in Dialogue Policy	A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning	49	2023	http://arxiv.org/abs/2202.13675v2	27,542
NLP	Contextualized Language Models in Machine Reading Comprehension	Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond	78	2020	http://arxiv.org/abs/2005.06249v1	71,397
NLP	Explainability in Machine Reading Comprehension	A Survey on Explainability in Machine Reading Comprehension	51	2020	http://arxiv.org/abs/2010.00389v1	26,035
LLMs (General)	Chain of Thought Reasoning in LLMs	Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future	228	2023	http://arxiv.org/abs/2309.15402v3	59,776
LLMs (General)	In-context Learning in LLMs	A Survey on In-context Learning	1,892	2022	https://arxiv.org/abs/2301.00234	35,769
Finance / Domain-specific	LLMs in Recommendation Systems	A Survey on Large Language Models for Recommendation	449	2024	https://arxiv.org/abs/2305.19860	22,986
LLMs Safety	LLM-Generated Content Detection	A Survey on Detection of LLMs-Generated Content	57	2023	https://arxiv.org/abs/2310.15654	41,035
Medical / Biomedical	LLMs in Medical Applications	A Survey of Large Language Models in Medicine: Progress, Application, and Challenge	158	2023	https://arxiv.org/abs/2311.05112	96,881
LLMs Safety	LLM Safety	Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements	10	2023	https://arxiv.org/abs/2302.09270	28,890
LLMs Safety	Hallucination in LLMs	A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions	1,557	2025	https://arxiv.org/abs/2311.05232	92,219
LLMs Safety	LLM Full Stack Safety	A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment	13	2025	https://arxiv.org/abs/2504.15585	161,502
Other	LLM-based Autonomous Agents	A Survey on Large Language Model based Autonomous Agents	1,446	2025	https://arxiv.org/abs/2308.11432	55,603
LLMs (General)	LLM Reasoning	Reasoning with Large Language Models, a Survey	82	2024	https://arxiv.org/abs/2407.11511	44,429
Multimodal	Vision-Language Models in Vision Tasks	Vision-Language Models for Vision Tasks: A Survey	696	2024	https://arxiv.org/abs/2304.00685	75,611
LLMs (General)	LLM Alignment Techniques	A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More	24	2024	https://arxiv.org/abs/2407.16216	73,556
Robotics	Deep Reinforcement Learning in Robotics	Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes	67	2025	https://arxiv.org/abs/2408.03539	102,954
LLMs Safety	Hallucination in LVMs	A Survey on Hallucination in Large Vision-Language Models	216	2024	https://arxiv.org/abs/2402.00253	17,647
LLMs Safety	LLM Security and Privacy	A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly	870	2024	https://arxiv.org/abs/2312.02003	47,825
Medical / Biomedical	Medical LLMs, Trustworthiness in LLMs	A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions	43	2024	https://arxiv.org/abs/2406.03712	61,934
Benchmarking / Evaluation	LLM Evaluation Methods	A Survey on LLM-as-a-Judge	163	2024	https://arxiv.org/abs/2411.15594	48,451
Finance / Domain-specific	LLMs in Finance Applications	Revolutionizing Finance with LLMs: An Overview of Applications and Insights	135	2024	https://arxiv.org/abs/2401.11641	29,116
LLMs (General)	Retrieval-Augmented Generation	Retrieval-Augmented Generation for Large Language Models: A Survey	2,184	2023	https://arxiv.org/abs/2312.10997	9,966
LLMs (General)	Mixture of Experts in LLMs	A Survey on Mixture of Experts in Large Language Models	138	2023	https://arxiv.org/abs/2407.06204	83,623
LLMs (General)	Multilingual LLMs	Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers	81	2024	https://arxiv.org/abs/2404.04925	81,148
Other	Continual Learning in AI	A Comprehensive Survey of Continual Learning: Theory, Method and Application	1,025	2024	https://arxiv.org/pdf/2302.00487	109,971
LLMs Efficiency	Parameter-Efficient Fine-Tuning	Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey	479	2024	https://arxiv.org/abs/2403.14608	61,858
Multimodal	Multimodal Reasoning in MLLMs	Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning	44	2024	https://arxiv.org/abs/2401.06805	49,225
Robotics	LLMs in Robotics	Large Language Models for Robotics: A Survey	160	2024	https://arxiv.org/abs/2311.07226	37,682
Multimodal	Vision-Language-Action Models in Embodied AI	A Survey on Vision-Language-Action Models for Embodied AI	77	2024	https://arxiv.org/abs/2405.14093	93,748
LLMs Safety	Red Teaming for Generative Models	Against The Achilles' Heel: A Survey on Red Teaming for Generative Models	22	2025	https://arxiv.org/abs/2404.00629	97,190

📊 Dataset Statistics

SurveyScope emphasizes coverage, recency, and impact, setting it apart from prior benchmarks. Below is a high-level summary:

• 📌 Diverse Topics
  11 active research areas, including NLP, LLMs, AI safety, robotics, and multimodal learning.

Distribution of topics in SurveyScope.

• 🕒 Recent Publications
  Focused on 2020–2025 publications to reflect the latest developments, especially in LLMs post-2022.

Publication year distribution.

• 📈 High Citation Impact
  Average: 322 citations/paper; 52% exceed 100 citations.

Citation distribution in SurveyScope.

---

📐 Evaluation Results

We evaluated SciSage against strong baselines:

• OpenScholar
• AutoSurvey
• LLM × MapReduce-V2

The evaluation covers content quality, structural coherence, and citation fidelity.

Automatic evaluation metrics across systems.

---

📎 Citation

If you find SurveyScope useful, please cite:

@misc{shi2025scisagemultiagentframeworkhighquality,
      title={SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation}, 
      author={Xiaofeng Shi and Qian Kou and Yuduo Li and Ning Tang and Jinxin Xie and Longbin Yu and Songjing Wang and Hua Zhou},
      year={2025},
      eprint={2506.12689},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.12689}, 
}