SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and
  Multi-dimensional Evaluation for Automated Survey Writing
Survey paper plays a crucial role in scientific research, especially given
the rapid growth of research publications. Recently, researchers have begun
using LLMs to automate survey generation for better efficiency. However, the
quality gap between LLM-generated surveys and those written by human remains
significant, particularly in terms of outline quality and citation accuracy. To
close these gaps, we introduce SurveyForge, which first generates the outline
by analyzing the logical structure of human-written outlines and referring to
the retrieved domain-related articles. Subsequently, leveraging high-quality
papers retrieved from memory by our scholar navigation agent, SurveyForge can
automatically generate and refine the content of the generated article.
Moreover, to achieve a comprehensive evaluation, we construct SurveyBench,
which includes 100 human-written survey papers for win-rate comparison and
assesses AI-generated survey papers across three dimensions: reference,
outline, and content quality. Experiments demonstrate that SurveyForge can
outperform previous works such as AutoSurvey.
67cfbab6607797f40c6d4164
Xiangchao Yan
admin_assigned
2025-03-11T14:39:44.870Z
65b88b92e0bde92c176a888a
/avatars/fc1cb54328ca93860e97fc73a3c1eb2f.svg
Xiangchao Yan
user
yxc97
67cfbab6607797f40c6d4165
Shiyang Feng
67cfbab6607797f40c6d4166
Jiakang Yuan
admin_assigned
2025-03-11T14:39:59.177Z
64a3d1ddb3239f3e3892b24b
/avatars/7ce585f5fc1d077fb1d70cc18c4da2c1.svg
Jiakang Yuan
user
JiakangYuan
67cfbab6607797f40c6d4167
Renqiu Xia
admin_assigned
2025-03-11T14:40:04.859Z
65b7ae76768464877cdb2e39
https://cdn-avatars.huggingface.co/v1/production/uploads/65b7ae76768464877cdb2e39/uAWPo4tpkqbZoDeEkc7y0.jpeg
Renqiu Xia
user
renqiux0302
67cfbab6607797f40c6d4168
Bin Wang
67cfbab6607797f40c6d4169
Bo Zhang
admin_assigned
2025-03-11T14:40:19.430Z
643dfd235aafbdca3a5792c0
/avatars/ce8553cf5936012c692e08054ee27937.svg
Bo Zhang
user
BoZhang
67cfbab6607797f40c6d416a
Lei Bai
SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and
  Multi-dimensional Evaluation for Automated Survey Writing
Survey paper plays a crucial role in scientific research, especially given
the rapid growth of research publications. Recently, researchers have begun
using LLMs to automate survey generation for better efficiency. However, the
quality gap between LLM-generated surveys and those written by human remains
significant, particularly in terms of outline quality and citation accuracy. To
close these gaps, we introduce SurveyForge, which first generates the outline
by analyzing the logical structure of human-written outlines and referring to
the retrieved domain-related articles. Subsequently, leveraging high-quality
papers retrieved from memory by our scholar navigation agent, SurveyForge can
automatically generate and refine the content of the generated article.
Moreover, to achieve a comprehensive evaluation, we construct SurveyBench,
which includes 100 human-written survey papers for win-rate comparison and
assesses AI-generated survey papers across three dimensions: reference,
outline, and content quality. Experiments demonstrate that SurveyForge can
outperform previous works such as AutoSurvey.
SurveyForge automates survey paper generation by refining outlines and content, outperforming previous methods in reference, outline, and content quality.
LLMs SurveyForge scholar navigation agent SurveyBench AutoSurvey