Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.10252 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914837348483072 |
|---|---|
| author | Wang, Yidong Guo, Qi Yao, Wenjin Zhang, Hongbo Zhang, Xin Wu, Zhen Zhang, Meishan Dai, Xinyu Zhang, Min Wen, Qingsong Ye, Wei Zhang, Shikun Zhang, Yue |
| author_facet | Wang, Yidong Guo, Qi Yao, Wenjin Zhang, Hongbo Zhang, Xin Wu, Zhen Zhang, Meishan Dai, Xinyu Zhang, Min Wen, Qingsong Ye, Wei Zhang, Shikun Zhang, Yue |
| contents | This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence. Traditional survey paper creation faces challenges due to the vast volume and complexity of information, prompting the need for efficient survey methods. While large language models (LLMs) offer promise in automating this process, challenges such as context window limitations, parametric knowledge constraints, and the lack of evaluation benchmarks remain. AutoSurvey addresses these challenges through a systematic approach that involves initial retrieval and outline generation, subsection drafting by specialized LLMs, integration and refinement, and rigorous evaluation and iteration. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.We open our resources at \url{https://github.com/AutoSurveys/AutoSurvey}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10252 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoSurvey: Large Language Models Can Automatically Write Surveys Wang, Yidong Guo, Qi Yao, Wenjin Zhang, Hongbo Zhang, Xin Wu, Zhen Zhang, Meishan Dai, Xinyu Zhang, Min Wen, Qingsong Ye, Wei Zhang, Shikun Zhang, Yue Information Retrieval Artificial Intelligence Computation and Language This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence. Traditional survey paper creation faces challenges due to the vast volume and complexity of information, prompting the need for efficient survey methods. While large language models (LLMs) offer promise in automating this process, challenges such as context window limitations, parametric knowledge constraints, and the lack of evaluation benchmarks remain. AutoSurvey addresses these challenges through a systematic approach that involves initial retrieval and outline generation, subsection drafting by specialized LLMs, integration and refinement, and rigorous evaluation and iteration. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.We open our resources at \url{https://github.com/AutoSurveys/AutoSurvey}. |
| title | AutoSurvey: Large Language Models Can Automatically Write Surveys |
| topic | Information Retrieval Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2406.10252 |