✨Welcome to SurveyX! This GitHub repository serves as a channel for users to submit requests for paper generation based on specific topics or domains.📚
SurveyX is an advanced academic survey automation system that leverages the power of Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys.🚀
By simply providing a paper title and keywords for literature retrieval, users can request comprehensive academic papers or surveys tailored to specific topics.
If you're curious about how SurveyX works or want to understand the underlying technology and methodology, feel free to check out our 📑website, where we provide an in-depth explanation of the system's architecture, data processing methods, and experimental results.
This GitHub repository is designed to provide a platform where users can request the generation of high-quality, domain-specific academic surveys by simply submitting an issue. The main purpose of this repository is to allow users to easily create and receive tailored academic surveys or papers, which are generated using SurveyX📄
By submitting an issue with a paper title and keywords for literature search, users can quickly generate a comprehensive review paper or survey on a specific topic. This process streamlines academic research by automating paper creation, saving users time and effort in compiling research content. 📚💡
To request a paper, create a new issue with the following details:
- Paper Title: Provide the title of the paper you need.
- Keywords for Literature Search: Provide keywords separated by commas that will help retrieve relevant literature and guide the content generation (e.g. "AI in healthcare, climate change impact on agriculture, ethical implications of AI").
- Your email(optional): Please provide your email address so that we can notify you promptly once the paper is ready.
Title: Controllable text generation of LLM: A Survey
Keywords: AI, healthcare, ethical implications, technology adoption, AI-driven diagnostics
Email: [email protected]
Once your request is submitted, the generated paper will be placed in the user requests folder. Please allow 1-2 business days for processing and generation. ⏳
| Title | Keywords |
|---|---|
| From BERT to GPT-4: A Survey of Architectural Innovations in Pre-trained Language Models | Transformer, BERT, GPT-3, self-attention, masked language modeling, cross-lingual transfer, model scaling |
| Unsupervised Cross-Lingual Word Embedding Alignment: Techniques and Applications | low-resource NLP, few-shot learning, data augmentation, unsupervised alignment, synthetic corpora, NLLB, zero-shot transfer |
| Vision-Language Pre-training: Architectures, Benchmarks, and Emerging Trends | multimodal learning, CLIP, Whisper, cross-modal retrieval, modality fusion, video-language models, contrastive learning |
| Efficient NLP at Scale: A Review of Model Compression Techniques | model compression, knowledge distillation, pruning, quantization, TinyBERT, edge computing, latency-accuracy tradeoff |
| Domain-Specific NLP: Adapting Models for Healthcare, Law, and Finance | domain adaptation, BioBERT, legal NLP, clinical text analysis, privacy-preserving NLP, terminology extraction, few-shot domain transfer |
| Attention Heads of Large Language Models: A Survey | attention head, attention mechanism, large language model, LLM,transformer architecture, neural networks, natural language processing |
| Controllable Text Generation for Large Language Models: A Survey | controlled text generation, text generation, large language model, LLM,natural language processing |
| A survey on evaluation of large language models | evaluation of large language models,large language models assessment, natural language processing, AI model evaluation |
| Large language models for generative information extraction: a survey | information extraction, large language models, LLM,natural language processing, generative AI, text mining |
| Internal consistency and self feedback of LLM | Internal consistency, self feedback, large language model, LLM,natural language processing, model evaluation, AI reliability |
| Review of Multi Agent Offline Reinforcement Learning | multi agent, offline policy, reinforcement learning,decentralized learning, cooperative agents, policy optimization |
| Reasoning of large language model: A survey | reasoning of large language models, large language models, LLM,natural language processing, AI reasoning, transformer models |
| Hierarchy Theorems in Computational Complexity: From Time-Space Tradeoffs to Oracle Separations | P vs NP, NP-completeness, polynomial hierarchy, space complexity, oracle separation, Cook-Levin theorem |
| Classical Simulation of Quantum Circuits: Complexity Barriers and Implications | BQP, quantum supremacy, Shor's algorithm, post-quantum cryptography, QMA, hidden subgroup problem |
| Kernelization: Theory, Techniques, and Limits | fixed-parameter tractable (FPT), kernelization, treewidth, W-hierarchy, ETH (Exponential Time Hypothesis), parameterized reduction |
| Optimal Inapproximability Thresholds for Combinatorial Optimization Problems | PCP theorem, approximation ratio, Unique Games Conjecture, APX-hardness, gap-preserving reduction, LP relaxation |
| Hardness in P: When Polynomial Time is Not Enough | SETH (Strong Exponential Time Hypothesis), 3SUM conjecture, all-pairs shortest paths (APSP), orthogonal vectors problem, fine-grained reduction, dynamic lower bounds |
| Consistency Models in Distributed Databases: From ACID to NewSQL | CAP theorem, ACID vs BASE, Paxos/Raft, Spanner, NewSQL, sharding, linearizability |
| Cloud-Native Databases: Architectures, Challenges, and Future Directions | cloud databases, AWS Aurora, Snowflake, storage-compute separation, auto-scaling, pay-per-query, multi-tenancy |
| Graph Database Systems: Storage Engines and Query Optimization Techniques | graph traversal, Neo4j, SPARQL, property graph, subgraph matching, RDF triplestore, Gremlin |
| Real-Time Aggregation in TSDBs: Techniques for High-Cardinality Data | time-series data, InfluxDB, Prometheus, downsampling, time windowing, high-cardinality indexing, stream processing |
| Self-Driving Databases: A Survey of AI-Powered Autonomous Management | autonomous databases, learned indexes, query optimization, Oracle AutoML, workload forecasting, anomaly detection |
| Multi-Model Databases: Integrating Relational, Document, and Graph Paradigms | multi-model database, MongoDB, ArangoDB, JSONB, unified query language, schema flexibility, polystore |
| Vector Databases for AI: Efficient Similarity Search and Retrieval-Augmented Generation | vector database, FAISS, Milvus, ANN search, embedding indexing, RAG (Retrieval-Augmented Generation), HNSW |
| Software-Defined Networking: Evolution, Challenges, and Future Scalability | OpenFlow, control plane/data plane separation, NFV orchestration, network slicing, P4 language, OpenDaylight, scalability bottlenecks |
| Beyond 5G: Architectural Innovations for Terahertz Communication and Network Slicing | network slicing, MEC (Multi-access Edge Computing), beamforming, mmWave, URLLC (Ultra-Reliable Low-Latency Communication), O-RAN, energy efficiency |
| IoT Network Protocols: A Comparative Study of LoRaWAN, NB-IoT, and Thread | LPWAN, LoRa, ZigBee 3.0, 6LoWPAN, TDMA scheduling, RPL routing, device density management |
| Edge Caching in Content Delivery Networks: Algorithms and Economic Incentives | CDN, Akamai, cache replacement policies, DASH (Dynamic Adaptive Streaming), QoE optimization, edge server placement, bandwidth cost reduction |
| A survey on flow batteries | battery electrolyte formulation |
| Research on battery electrolyte formulation | flow batteries |
| Title | Keywords |
|---|---|
| Reasoning of large language model: A Survey | reasoning, LLM, NLP, Artificial Intelligence (AI) |
| Think_and_Draw!_A_survey_on_Vision-MLLMs_that_can_understand_and_generate | vision-language models, multimodal learning, generative AI |
| A Survey of new intent detection and discovery for Conversational Understanding | Out-of-domain Detection, New Intent Discovery, Generalized Category Discovery |
Please cite us if you find this project helpful for your project/paper:
@misc{liang2025surveyxacademicsurveyautomation,
title={SurveyX: Academic Survey Automation via Large Language Models},
author={Xun Liang and Jiawei Yang and Yezhaohui Wang and Chen Tang and Zifan Zheng and Simin Niu and Shichao Song and Hanyu Wang and Bo Tang and Feiyu Xiong and Keming Mao and Zhiyu li},
year={2025},
eprint={2502.14776},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.14776},
}
Our retrieval engine may not have access to many papers that require commercial licensing. If your research topic requires papers from sources other than arXiv, the quality and comprehensiveness of the generated papers may be affected due to limitations in our retrieval scope.
SurveyX uses advanced language models to assist with the generation of academic papers. However, it is important to note that the generated content is a tool for research assistance. Users should verify the accuracy of the generated papers, as SurveyX cannot guarantee full compliance with academic standards.