AutoSurvey: Large Language Models Can Automatically Write Surveys (2406.10252v2)

Abstract: 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 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}.

• The paper introduces AutoSurvey, a framework that leverages LLMs to automate comprehensive literature surveys through a systematic four-step process.
• It employs an embedding-based retrieval strategy, parallel LLM drafting, and multi-LLM evaluation to overcome context window and knowledge constraints.
• Experimental results demonstrate near-human performance with 82.25% recall and 77.41% precision in citation quality, underscoring its effectiveness in automating survey creation.

Overview of AutoSurvey: Automated Survey Writing with LLMs

The paper presents AutoSurvey, a methodology leveraging LLMs to automate the creation of comprehensive literature surveys. The need for such automated systems arises from the rapid pace of scientific development, particularly in fields like artificial intelligence, where the volume of publications is increasing exponentially. Traditional survey creation methods are strained by this deluge of information, necessitating more efficient ways to synthesize existing literature.

Core Contributions

The authors introduce AutoSurvey as a streamlined process addressing key challenges in automated survey generation with LLMs, specifically context window limitations and parametric knowledge constraints. The methodology involves a four-step process:

1. Initial Retrieval and Outline Generation: AutoSurvey uses embedding-based retrieval techniques to identify and organize relevant literature into a coherent outline, which forms the basis for the survey.
2. Subsection Drafting: Specialized LLMs draft each section of the survey in parallel, guided by the structured outline. This parallelization accelerates the survey generation process while maintaining focus and detail.
3. Integration and Refinement: The drafted sections undergo a refinement phase, ensuring coherence and logical flow across the survey. Sections are systematically merged into a cohesive document.
4. Rigorous Evaluation and Iteration: A Multi-LLM-as-Judge strategy is employed to evaluate the survey critically. This ensures that the generated surveys adhere to high academic standards in terms of citation accuracy and content quality.

Evaluation and Results

The experimental results demonstrate that AutoSurvey significantly outperforms naive RAG-based LLM methods in both citation quality and content quality. For instance, a 64k-token survey generated by AutoSurvey achieved an 82.25% recall and 77.41% precision in citation quality, closely approaching human performance levels (86.33% recall and 77.78% precision). In terms of content quality, AutoSurvey scored highly across metrics such as coverage, structure, and relevance, again nearing human benchmarks.

The authors also conducted a meta-evaluation comparing AutoSurvey’s evaluations with those of human experts, showing a moderate to strong positive correlation, suggesting the evaluation framework aligns well with human judgment.

Implications and Future Directions

AutoSurvey provides a scalable, efficient solution for synthesizing research literature, particularly beneficial in domains experiencing rapid scientific advancements. By automating the survey writing process, this methodology not only saves significant time but also potentially democratizes access to comprehensive literature reviews, enabling more widespread dissemination of synthesized academic knowledge.

Furthermore, AutoSurvey lays a foundation for future research in leveraging LLMs for extensive academic writings. As LLM capabilities continue to expand, there is potential for further reducing the time and improving the quality of automated academic surveys, making them even more comparable to human-authored reviews.

The work also opens discussions on how automated survey generation can be coupled with real-time knowledge updates and more robust evaluation benchmarks, suggesting a future where dynamic survey documents react immediately to new research outputs.

Conclusion

AutoSurvey represents a significant step toward integrating AI effectively in academic literature synthesis. While there are inherent limitations, particularly concerning the fidelity of citations and the continuous evolution of LLMs, the framework provided by AutoSurvey is a versatile and valuable tool in managing and understanding the ever-expanding landscape of academic research.