SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models
This work addresses the lack of standardized evaluation for scientific survey generation, which is an incremental improvement for researchers and practitioners in document processing.
The authors tackled the problem of evaluating and improving automatic scientific survey generation by creating SurveyGen, a dataset of over 4,200 human-written surveys with quality metadata, and developed QUAL-SG, a quality-aware framework that enhances retrieval to select higher-quality source papers. Results showed that semi-automatic pipelines can be partially competitive, but fully automatic generation still suffers from low citation quality and limited critical analysis.
Automatic survey generation has emerged as a key task in scientific document processing. While large language models (LLMs) have shown promise in generating survey texts, the lack of standardized evaluation datasets critically hampers rigorous assessment of their performance against human-written surveys. In this work, we present SurveyGen, a large-scale dataset comprising over 4,200 human-written surveys across diverse scientific domains, along with 242,143 cited references and extensive quality-related metadata for both the surveys and the cited papers. Leveraging this resource, we build QUAL-SG, a novel quality-aware framework for survey generation that enhances the standard Retrieval-Augmented Generation (RAG) pipeline by incorporating quality-aware indicators into literature retrieval to assess and select higher-quality source papers. Using this dataset and framework, we systematically evaluate state-of-the-art LLMs under varying levels of human involvement - from fully automatic generation to human-guided writing. Experimental results and human evaluations show that while semi-automatic pipelines can achieve partially competitive outcomes, fully automatic survey generation still suffers from low citation quality and limited critical analysis.