SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models

TL;DR

SurveyGen introduces a large dataset and a quality-aware framework for scientific survey generation using large language models, highlighting current limitations in fully automatic approaches and emphasizing the importance of quality assessment.

Contribution

The paper provides the first large-scale survey dataset with quality metadata and a novel quality-aware retrieval framework for improved survey generation with LLMs.

Findings

Semi-automatic pipelines achieve partially competitive results.

Fully automatic generation has issues with citation quality.

Human involvement improves survey quality.

Abstract

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.