Feedback Is The Key for Automated Survey Generation

摘要

The escalating demand for comprehensive literature surveys in rapidly evolving research areas makes manual writing increasingly impractical, underscoring the necessity of automation. Large Language Models (LLMs) provide a promising foundation for this task, yet guiding them to generate accurate, reliable content remains a fundamental challenge, as issues such as hallucinations and vague organization often persist. To address this, we propose FIKSurvey, a feedback-driven framework grounded in the idea that “Feedback is the key for automatic survey generation.” Specifically, FIKSurvey systematically incorporates feedback across three dimensions: outline feedback for structural clarity, citation feedback for evidence validation, and content feedback for readability and analytical depth. The framework also supports optional human-in-the-loop intervention for user-specific needs. Experiments confirm that FIKSurvey substantially improves both citation and content quality, demonstrating feedback as the critical mechanism for automatic survey generation.